OSM report 2022-2023
Marissa Dyck
2024-05-08
The first two chunks of this r markdown file after the r setup allow for plot zooming, but it also means that the html file must be opened in a browser to view the document properly. When it knits in RStudio the preview will appear empty but the html when opened in a browser will have all the info and you can click on each plot to Zoom in on it.
Before you begin
Notes
A few notes about this script.
If you are running this with the 2022-2023 data make sure you download the whole (OSM_2022-2023 GitHub repository)[https://github.com/ACMElabUvic/OSM_2022-2023] from the ACMElabUvic GitHub. This will ensure you have all the files, data, and proper folder structure you will need to run this code and associated analyses.
Also make sure you open RStudio through the R project (OSM_2022-2023.Rproj) this will automatically set your working directory to the correct place (wherever you saved the repository) and ensure you don’t have to change the file paths for some of the data.
Lastly, if you are looking to adapt this code for a future year of data, you will want to ensure you have run the 2_ACME_landscape_covariate_exploration_script.Rmd with your data as there is much data formatting, cleaning, and restructuring that has to be done before this code will work. Helpful note: The files are numbered in the order they are used for this analysis.
If you have question please email the most recent author, currently
Marissa A. Dyck
Postdoctoral research fellow
University of Victoria
School of Environmental Studies
Email: marissadyck17@gmail.com
(update/add authors as needed)
R and RStudio
Before starting you should ensure you have the latest version of R and RStudio downloaded. This code was generated under R version 4.2.3 and with RStudio version 2024.04.2+764.
You can download R and RStudio HERE
R markdown
This script is written in R markdown and thus uses a mix of coding markup languages and R. If you are planning to run this script with new data or make any modifications you will want to be familiar with some basics of R markdown.
Below is an R markdown cheatsheet to help you get started,
R
markdown cheatsheet
Install packages
If you don’t already have the following packages installed, use the code below to install them. *NOTE this will not run automatically as eval=FALSE is included in the chunk setup (i.e. I don’t want it to run every time I run this code since I have the packages installed).
install.packages('tidyverse')
install.packages('ggpubr')
install.packages('corrplot')
install.packages('Hmisc')
install.packages('glmmTMB')
install.packages('MuMIn')
install.packages('TMB', type = 'source')
install.packages('rphylopic')
install.packages('broom')Load libraries
Then load the packages to your library so they are usable for this session.
library(tidyverse) # data tidying, visualization, and much more; this will load all tidyverse packages, can see complete list using tidyverse_packages()
library(ggpubr) # make modifications to plot for publication (arrange plots)
library(PerformanceAnalytics) # Used to generate a correlation plot
library(Hmisc) # used to generate histograms for all variables in data frame
library(glmmTMB) # Constructing GLMMs## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.4.1
## Current Matrix version is 1.5.3
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' packagelibrary(MuMIn) # for model selection
library(rphylopic) # add animal silhouettes to graphs
library(broom) # extracting odds ratios in a tidy formatData
Load detection data
Read in saved and cleaned detection data for the 6 LUs from 2021-2022 and 2022-2023 e.g., the 1_ACME_camera_script_9-2-2024.R.
These are two separate files from the two different fiscal years, so they need to be imported and then merged into one data file for plotting. Since both are stored in the data/processed/ folder we can read them both in as a list with purrr.
# detection data
# read in saved and cleaned detection data from the 1_ACME_camera_script_9-2-2024.R
detections <- file.path('data/processed',
c('OSM_ind_det_2021.csv',
'OSM_ind_det_2022.csv')) %>%
map(~.x %>%
read_csv(.) %>%
# ensure the array, site, species, and event_id read in as factors
mutate_if(is.character,
as.factor)) %>%
# give names to each data frame in list
purrr::set_names('dets_2021',
'dets_2022') # R doesn't like when they are just numbers, you can make it work but it's annoying to call the data frame later so I've called them dets_year## Rows: 6696 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): array, site, species, event_id
## dbl (3): month, year, timediff
## dttm (1): datetime
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 14063 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): array, site, species, event_id
## dbl (3): month, year, timediff
## dttm (1): datetime
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# look at data structure
str(detections)## List of 2
## $ dets_2021: tibble [6,696 × 8] (S3: tbl_df/tbl/data.frame)
## ..$ array : Factor w/ 2 levels "LU2","LU3": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ site : Factor w/ 78 levels "LU2_03","LU2_05",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..$ species : Factor w/ 35 levels "Arctic hare",..: 28 28 35 35 35 35 35 35 35 35 ...
## ..$ datetime: POSIXct[1:6696], format: "2021-07-15 11:40:07" "2022-02-06 15:11:06" ...
## ..$ month : num [1:6696] 7 2 7 8 8 8 8 8 8 9 ...
## ..$ year : num [1:6696] 2021 2022 2021 2021 2021 ...
## ..$ timediff: num [1:6696] NA 296839 NA 28519 13230 ...
## ..$ event_id: Factor w/ 6696 levels "E1000000","E1000001",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ dets_2022: tibble [14,063 × 8] (S3: tbl_df/tbl/data.frame)
## ..$ array : Factor w/ 4 levels "LU01","LU13",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..$ site : Factor w/ 155 levels "LU01_06","LU01_10",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..$ species : Factor w/ 39 levels "ATVer","Beaver",..: 31 31 38 38 38 38 38 38 38 38 ...
## ..$ datetime: POSIXct[1:14063], format: "2022-06-17 10:01:52" "2023-09-10 12:51:15" ...
## ..$ month : num [1:14063] 6 9 6 7 7 7 8 8 8 8 ...
## ..$ year : num [1:14063] 2022 2023 2022 2022 2022 ...
## ..$ timediff: num [1:14063] NA 648166 NA 31847 21429 ...
## ..$ event_id: Factor w/ 14063 levels "E0","E1","E10",..: 1 2 5176 6287 7398 8509 9620 10731 11842 12953 ...Merge data
Now we need to merge these two files to make plotting them easier.
They have the same columns so we could just the base R
rbind() function, but in case there are differences in the
columns in the future, let’s use the cleaner
dplyr::bind_rows() function
# Join two years of detection data
detections_merged <- dplyr::bind_rows(detections$dets_2021,
detections$dets_2022)
# check structure of new data
str(detections_merged)## tibble [20,759 × 8] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 6 levels "LU2","LU3","LU01",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 233 levels "LU2_03","LU2_05",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ species : Factor w/ 42 levels "Arctic hare",..: 28 28 35 35 35 35 35 35 35 35 ...
## $ datetime: POSIXct[1:20759], format: "2021-07-15 11:40:07" "2022-02-06 15:11:06" ...
## $ month : num [1:20759] 7 2 7 8 8 8 8 8 8 9 ...
## $ year : num [1:20759] 2021 2022 2021 2021 2021 ...
## $ timediff: num [1:20759] NA 296839 NA 28519 13230 ...
## $ event_id: Factor w/ 20759 levels "E1000000","E1000001",..: 1 2 3 4 5 6 7 8 9 10 ...Data checks
Check to make sure the data looks good after merging before moving on.
Arrays and sites
Let’s check that there are the correct number of levels for arrays and sites, there should be 6 arrays and 233 sites (155 from 2022-2023 and 78 from 2021-2022).
# this will provide a summary of the levels
str(detections_merged)## tibble [20,759 × 8] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 6 levels "LU2","LU3","LU01",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 233 levels "LU2_03","LU2_05",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ species : Factor w/ 42 levels "Arctic hare",..: 28 28 35 35 35 35 35 35 35 35 ...
## $ datetime: POSIXct[1:20759], format: "2021-07-15 11:40:07" "2022-02-06 15:11:06" ...
## $ month : num [1:20759] 7 2 7 8 8 8 8 8 8 9 ...
## $ year : num [1:20759] 2021 2022 2021 2021 2021 ...
## $ timediff: num [1:20759] NA 296839 NA 28519 13230 ...
## $ event_id: Factor w/ 20759 levels "E1000000","E1000001",..: 1 2 3 4 5 6 7 8 9 10 ...# this will list all unique entries (levels) for each variable
levels(detections_merged$array)## [1] "LU2" "LU3" "LU01" "LU13" "LU15" "LU21"levels(detections_merged$site)## [1] "LU2_03" "LU2_05" "LU2_100" "LU2_101" "LU2_106" "LU2_110"
## [7] "LU2_119" "LU2_123" "LU2_13" "LU2_137" "LU2_143" "LU2_15"
## [13] "LU2_153" "LU2_155" "LU2_17" "LU2_18" "LU2_20" "LU2_21"
## [19] "LU2_24" "LU2_25" "LU2_26" "LU2_27" "LU2_29" "LU2_30"
## [25] "LU2_31" "LU2_32" "LU2_33" "LU2_34" "LU2_36" "LU2_38"
## [31] "LU2_39" "LU2_40" "LU2_42" "LU2_44" "LU2_46" "LU2_47"
## [37] "LU2_50" "LU2_51" "LU2_52" "LU2_54" "LU2_56" "LU2_57"
## [43] "LU3_05" "LU3_07" "LU3_10" "LU3_102" "LU3_103" "LU3_111"
## [49] "LU3_126" "LU3_129" "LU3_13" "LU3_131" "LU3_137" "LU3_14"
## [55] "LU3_147" "LU3_15" "LU3_17" "LU3_18" "LU3_19" "LU3_20"
## [61] "LU3_21" "LU3_25" "LU3_27" "LU3_28" "LU3_32" "LU3_36"
## [67] "LU3_38" "LU3_39" "LU3_40" "LU3_41" "LU3_44" "LU3_45"
## [73] "LU3_46" "LU3_48" "LU3_49" "LU3_50" "LU3_51" "LU3_52"
## [79] "LU01_06" "LU01_10" "LU01_11" "LU01_13" "LU01_22" "LU01_25"
## [85] "LU01_27" "LU01_30" "LU01_32" "LU01_36" "LU01_40" "LU01_41"
## [91] "LU01_43" "LU01_44" "LU01_45" "LU01_46" "LU01_47" "LU01_48"
## [97] "LU01_60" "LU01_63" "LU01_64" "LU01_66" "LU01_67" "LU01_70"
## [103] "LU01_71" "LU01_72" "LU01_73" "LU01_74" "LU01_75" "LU01_76"
## [109] "LU01_77" "LU01_78" "LU01_79" "LU01_80" "LU01_82" "LU01_83"
## [115] "LU01_84" "LU01_85" "LU01_86" "LU13_03" "LU13_05" "LU13_06"
## [121] "LU13_08" "LU13_11" "LU13_12" "LU13_128" "LU13_13" "LU13_131"
## [127] "LU13_14" "LU13_15" "LU13_16" "LU13_17" "LU13_18" "LU13_19"
## [133] "LU13_20" "LU13_21" "LU13_22" "LU13_26" "LU13_27" "LU13_30"
## [139] "LU13_32" "LU13_33" "LU13_34" "LU13_35" "LU13_36" "LU13_37"
## [145] "LU13_38" "LU13_41" "LU13_43" "LU13_45" "LU13_47" "LU13_49"
## [151] "LU13_51" "LU13_52" "LU13_53" "LU13_55" "LU13_56" "LU13_57"
## [157] "LU13_59" "LU13_70" "LU15_01" "LU15_02" "LU15_03" "LU15_04"
## [163] "LU15_07" "LU15_08" "LU15_09" "LU15_10" "LU15_11" "LU15_12"
## [169] "LU15_14" "LU15_15" "LU15_16" "LU15_17" "LU15_18" "LU15_19"
## [175] "LU15_20" "LU15_21" "LU15_22" "LU15_23" "LU15_24" "LU15_25"
## [181] "LU15_26" "LU15_27" "LU15_28" "LU15_29" "LU15_30" "LU15_31"
## [187] "LU15_32" "LU15_34" "LU15_36" "LU15_37" "LU15_40" "LU15_41"
## [193] "LU15_43" "LU15_44" "LU15_46" "LU15_58" "LU15_61" "LU21_06"
## [199] "LU21_09" "LU21_10" "LU21_100" "LU21_105" "LU21_106" "LU21_107"
## [205] "LU21_109" "LU21_114" "LU21_116" "LU21_119" "LU21_122" "LU21_126"
## [211] "LU21_14" "LU21_153" "LU21_16" "LU21_164" "LU21_21" "LU21_23"
## [217] "LU21_27" "LU21_32" "LU21_36" "LU21_41" "LU21_52" "LU21_56"
## [223] "LU21_57" "LU21_59" "LU21_63" "LU21_68" "LU21_74" "LU21_78"
## [229] "LU21_82" "LU21_871" "LU21_93" "LU21_97" "LU21_98"Everything looks good.
NAs
Let’s ensure no NAs were introduced where there shouldn’t be during the joining process.
# check for NAs introduced during data merge
summary(detections_merged)## array site species
## LU2 :4711 LU01_47: 405 White-tailed deer:6141
## LU3 :1985 LU01_84: 396 Snowshoe hare :4571
## LU01:6283 LU01_66: 383 Red squirrel :2201
## LU13:1972 LU01_27: 320 Black bear :1997
## LU15:2951 LU2_39 : 310 Coyote :1285
## LU21:2857 LU2_50 : 292 Moose : 693
## (Other):18653 (Other) :3871
## datetime month year
## Min. :2021-07-08 10:21:40.00 Min. : 1.000 Min. :2021
## 1st Qu.:2022-04-25 01:14:32.50 1st Qu.: 5.000 1st Qu.:2022
## Median :2022-11-03 19:10:30.00 Median : 8.000 Median :2022
## Mean :2022-10-09 18:09:20.27 Mean : 7.204 Mean :2022
## 3rd Qu.:2023-05-05 01:40:45.50 3rd Qu.: 9.000 3rd Qu.:2023
## Max. :2023-10-02 11:08:57.00 Max. :12.000 Max. :2023
##
## timediff event_id
## Min. : 30 E1000000: 1
## 1st Qu.: 1427 E1000001: 1
## Median : 5270 E1000002: 1
## Mean : 30292 E1000003: 1
## 3rd Qu.: 18443 E1000004: 1
## Max. :653883 E1000005: 1
## NA's :2446 (Other) :20753THe only NAs are in the timediff column which is what we expect since any of the first observations won’t have a a value for timediff. If you are confused by this re-visit the 1_ACME_camera_script.
Summary
Some summaries of the merged detection data
focal_species <- c('Black bear',
'Caribou',
'Coyote',
'Fisher',
'Grey wolf',
'Lynx',
'Moose',
'Red fox',
'White-tailed deer')
focal_mammals <- detections_merged %>%
filter(species %in% focal_species)Data Formatting
In order to get plots that have the same formatting as last years’ report we have to do a bit of data formatting. First we need to make sure we are including the same relevant species (some were ignored for last years’ report or grouped together).
Last years report had the following species
- white-tailed deer
- snowshoe hare
- black bear
- coyote
- red squirrel
- fisher
- unknown
- moose
- lynx
- spruce grouse
- red fox
- striped skunk
- ruffed grouse
- owl
- grey wolf
- domestic dog
- cougar
- raven
- other
- mule deer
And they grouped all humans except for staff as ‘Humans’. Let’s look at the species we have in the combined years of data and try to format it the same way.
detections_merged %>%
# group by array and species
group_by(species) %>%
summarise(n = n()) %>%
# sort from greatest to least
arrange(desc(n)) %>%
# have R print everything
print(n = nrow(.))## # A tibble: 42 × 2
## species n
## <fct> <int>
## 1 White-tailed deer 6141
## 2 Snowshoe hare 4571
## 3 Red squirrel 2201
## 4 Black bear 1997
## 5 Coyote 1285
## 6 Moose 693
## 7 Unknown 663
## 8 Lynx 525
## 9 Staff 461
## 10 Unknown deer 340
## 11 Fisher 257
## 12 Grey wolf 224
## 13 Marten 220
## 14 Other birds 219
## 15 Red fox 127
## 16 Caribou 115
## 17 Spruce grouse 93
## 18 Ruffed grouse 90
## 19 Unknown canid 76
## 20 Grey jay 66
## 21 Unknown mustelid 66
## 22 Striped skunk 65
## 23 ATVer 41
## 24 Cougar 37
## 25 Unknown ungulate 34
## 26 Short-tailed weasel 19
## 27 Long-tailed weasel 17
## 28 Human 16
## 29 Snowmobiler 16
## 30 Domestic dog 15
## 31 Owl 15
## 32 Other 11
## 33 Raven 10
## 34 Wolverine 8
## 35 Otter 7
## 36 Porcupine 5
## 37 Beaver 4
## 38 Canada goose 4
## 39 Mule deer 2
## 40 Arctic hare 1
## 41 Elk 1
## 42 Hunter 1Hmmm there is one instance of an arctic hare, check that this isn’t meant to be a snowshoe hare and fix later if needed.
Now let’s create a new data frame (tibble) to work with for the OSM figure summaries specifically
I personally would lump all the unknown together and all the birds together but for the sake of consistency with last year’s figures we will remove the same entries and keep the birds separate, let’s create a vector of entries to drop
# create vector of entries to drop for plots
species_drop <- c('Staff',
'Unknown deer',
'Unknown ungulate',
'Unknown canid',
'Unknown mustelid',
'Other birds',
'Arctic hare')
# now we can create the new data frame with some changes consistent w/ choices made for 2021-2022
detections_merged <- detections_merged %>%
# for summarizing, lets lump all the recreational humans into "Humans"
mutate(species = recode_factor(species,
"Snowmobiler" = "Human",
"ATVer" = "Human",
'Hunter' = 'Human')) %>%
# remove species we don't want to plot
filter(!species %in% species_drop)
# look at data
str(detections_merged)## tibble [19,562 × 8] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 6 levels "LU2","LU3","LU01",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 233 levels "LU2_03","LU2_05",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ species : Factor w/ 39 levels "Human","Arctic hare",..: 33 33 33 33 33 33 33 33 33 33 ...
## $ datetime: POSIXct[1:19562], format: "2021-07-18 08:59:30" "2021-08-07 04:21:14" ...
## $ month : num [1:19562] 7 8 8 8 8 8 8 9 9 9 ...
## $ year : num [1:19562] 2021 2021 2021 2021 2021 ...
## $ timediff: num [1:19562] NA 28519 13230 4290 7337 ...
## $ event_id: Factor w/ 20759 levels "E1000000","E1000001",..: 3 4 5 6 7 8 9 10 11 12 ...Subset data
We will also want to subset the data by landscape unit (LU) and generate a new data frame for each LU to use for plotting
I’m not great at writing loops, so let’s see how this shit goes… probably bad but who knows
array_frames <- list()
for (i in unique(detections_merged$array)){
#Subset data based on radius
df <- detections_merged %>%
filter(array == i)
# list of dataframes
array_frames <- c(array_frames, list(df))
}
# inspect one data frame
print(array_frames[[1]]) # this is for LU2## # A tibble: 4,592 × 8
## array site species datetime month year timediff event_id
## <fct> <fct> <fct> <dttm> <dbl> <dbl> <dbl> <fct>
## 1 LU2 LU2_03 White-tailed … 2021-07-18 08:59:30 7 2021 NA E1000002
## 2 LU2 LU2_03 White-tailed … 2021-08-07 04:21:14 8 2021 28519. E1000003
## 3 LU2 LU2_03 White-tailed … 2021-08-16 08:51:21 8 2021 13230. E1000004
## 4 LU2 LU2_03 White-tailed … 2021-08-19 08:21:29 8 2021 4290. E1000005
## 5 LU2 LU2_03 White-tailed … 2021-08-24 10:39:23 8 2021 7337. E1000006
## 6 LU2 LU2_03 White-tailed … 2021-08-26 09:17:02 8 2021 2797. E1000007
## 7 LU2 LU2_03 White-tailed … 2021-08-31 19:13:33 8 2021 7796 E1000008
## 8 LU2 LU2_03 White-tailed … 2021-09-10 05:03:31 9 2021 13550. E1000009
## 9 LU2 LU2_03 White-tailed … 2021-09-16 17:10:41 9 2021 9363. E1000010
## 10 LU2 LU2_03 White-tailed … 2021-09-16 19:19:24 9 2021 127 E1000011
## # ℹ 4,582 more rows… I think this worked
Now let’s change names of list items using purrr, couldn’t figure out how to name them in the loop, you don’t necessarily need to do this because we change the names in the next section, but I like having things named
array_frames <- array_frames %>%
purrr::set_names('LU02',
'LU03',
'LU01',
'LU13',
'LU15',
'LU21')
# inspect each data frame
head(array_frames$LU01)## # A tibble: 6 × 8
## array site species datetime month year timediff event_id
## <fct> <fct> <fct> <dttm> <dbl> <dbl> <dbl> <fct>
## 1 LU01 LU01_06 White-tailed … 2022-06-18 11:09:19 6 2022 NA E2
## 2 LU01 LU01_06 White-tailed … 2022-07-10 13:56:10 7 2022 31847. E3
## 3 LU01 LU01_06 White-tailed … 2022-07-25 11:04:44 7 2022 21429. E4
## 4 LU01 LU01_06 White-tailed … 2022-07-31 06:38:06 7 2022 8356. E5
## 5 LU01 LU01_06 White-tailed … 2022-08-01 09:45:28 8 2022 1627. E6
## 6 LU01 LU01_06 White-tailed … 2022-08-01 15:51:01 8 2022 364. E7Detection plots
Detection data
Now we can apply the same data formatting for each LUs’ data frame using purrr.
We want to count the number of independent detections per species per LU to use in the detection plots
# apply the same formatting to each LU data frame using purrr map
detection_data <- array_frames %>%
purrr::map(
~.x %>%
# group by species
group_by(species) %>%
# calculate a column with unique accounts of each species
mutate(count = n_distinct(event_id)) %>%
# keep just the columns we need
select(species, count) %>%
# keep only unique (distinct) rows so we should be left with one row per species, this helps with plotting later if you don't do it ggplot will try to count and plot each row it's annoying
distinct()) %>%
# set names of list objects
purrr::set_names('Detections LU02',
'Detections LU03',
'Detections LU01',
'Detections LU13',
'Detections LU15',
'Detections LU21')Detection plots
Now to graph independent detections for each LU using purrr, this avoids a TON of code repetition needed to plot each one individually
We use purrr::imap() instead of
purrr::map() because imap maintains the variable names in
our list (e.g. Detections LU01, Detections LU13, etc.) which we can then
use to title each plot.
Within purrr::imap() we just paste the code we would use
for a single ggplot since all the graphical elements (except the title
which we change with the file name [.y]) are the same
# create object detection plots which uses the detection_data list (w/ all 4 LUs)
detection_plots <- detection_data %>%
# use imap instead of map as it allows us to use .y to paste the list element names as the plot titles later
purrr::imap(
~.x %>%
# now just copy and paste the ggplot code for the detection graphs
ggplot(.,
aes(x = reorder(species, count), y = count)) +
# plot as bar graph using geom_col so we don't have to provide a y aesthetic
geom_col() +
# switch the x and y axis
coord_flip() +
# add the number of detections at the end of each bar
geom_text(aes(label = count),
color = "black",
size = 3,
hjust = -0.3,
vjust = 0.2) +
# label x and y axis with informative titles
labs(x = 'Species',
y = 'Number of Independent (30 min) Detections') +
# add title to plot with LU name the .y will take the name of whatever you named each list element in the detection_data list, so make sure this name is what you want on the ggtitle
ggtitle(.y) +
# set the theme
theme_classic() +
theme(plot.title = element_text(hjust = 0.5)))
# view plots, this will print each in it's own window so you have to scroll back in the plot viewer pane to look at each one
detection_plots## $`Detections LU02`
##
## $`Detections LU03`
##
## $`Detections LU01`
##
## $`Detections LU13`
##
## $`Detections LU15`
##
## $`Detections LU21`
Save detection plots
Now we want to save these plots in case we need each individual one (we will combine the detection and naive occ plots into a single figure for each LU later and use those for the OSM report, but we may want these standalone plots later so let’s save them while they are here).
We can save all the plots from the purrr iteration above using
purrr::imap. imap is used instead of map because it allows
us to retain the list object names (plot names) to paste as the file
name with the .y command.
IMPORTANT if you are using this code for a future github repo, DO NOT use .tiff as the file extension. This will cause issues when trying to push any changes to the github repo as the files are too large to meet githubs requirements
# save plots only use if needed
purrr::imap(
detection_plots,
~ggsave(.x,
file = paste0("figures/OSM_",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 11,
height = 9,
units = 'in'))Naive occupancy
Data
We also need to alter the detection data a bit to use for naive occupancy plots.
We will use the individual LU detection data like we did before and
use purrr::map() to apply the dame data formatting to all 4
data frames.
Here we want to calculate the total number of sites in each LU, the number of sites each species was detected at in each LU and then use both those numbers to calculate naive occupancy for each species in each LU
# First we need to alter the data frame a bit for these plots, let's create a data frame for each LU (I couldn't figure out how to do this without assigning individual data frames for each UGH)
# apply the same formatting to each data frame using purrr
occupancy_data <- array_frames %>%
purrr::map(
~.x %>%
# calculate the total number of sites for each LU
mutate(total_sites = n_distinct(site)) %>%
# group by species to calculate the number of sites each spp occurred at
group_by(species) %>%
# add columns to count the number of sites each spp occurred at and then the naive occupancy
reframe(count = n_distinct(site),
naive_occ = count/total_sites,
ind_det = n_distinct(event_id)) %>%
# keep just the columns we need
select(species, naive_occ, ind_det) %>%
# keep only unique (distinct) rows so we should be left with one row per species, this helps with plotting
distinct()) %>%
purrr::set_names('Naive Occupancy LU02',
'Naive Occupancy LU03',
'Naive Occupancy LU01',
'Naive Occupancy LU13',
'Naive Occupancy LU15',
'Naive Occupancy LU21')Occupancy plots
Now we can graph naive occupancy for each LU using purrr, and as with the detection plots this saves a massive amount of coding using purrr to run an iteration on the data files and produce four plots at once instead of copying and pasting code for each individually
# create object occupancy_plots which uses the occupancy_data list (w/ all 4 LUs)
occupancy_plots <- occupancy_data %>%
# use imap instead of map as it allows us to use .y to paste the list element names as the plot titles later
purrr::imap(
~.x %>%
# now just copy and paste the ggplot code for the occupancy graphs
ggplot(.,
aes(x = fct_reorder(species,
ind_det), # this reorders the species so they match the order of the detection plot which makes it better for viewing when the plots are arranged together in 1 figure for each LU
y = naive_occ)) +
# plot as bars using geom_col() which uses stat = 'identity', instead of geom_bar() which will count the rows in each group and plot that instead of naive occ
geom_col() +
# flip x and y axis
coord_flip() +
# add text to end of bars that provides naive occ value
geom_text(aes(label = round(naive_occ, 2)),
size = 3,
hjust = -0.3,
vjust = 0.2) +
# relabel x and y axis and title
labs(x = 'Species',
y = 'Proportion of Sites With At Least One Detection') +
# set plot title using .y (name of list object)
ggtitle(.y) +
# set. theme elements
theme_classic()+
theme(plot.title = element_text(hjust = 0.5)))
# view plots
occupancy_plots## $`Naive Occupancy LU02`
##
## $`Naive Occupancy LU03`
##
## $`Naive Occupancy LU01`
##
## $`Naive Occupancy LU13`
##
## $`Naive Occupancy LU15`
##
## $`Naive Occupancy LU21`
Save occupancy plots
As with the detection plots, we might want these individual plots
later for something so we can use purrr::imap() to save
them to the figures folder
Again avoid using the .tiff extension in github
# save plots
purrr::imap(
occupancy_plots,
~ggsave(.x,
file = paste0("figures/OSM_",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 11,
height = 9,
units = 'in'))Final combined plots for report
The previous year’s report had a figure for each LU with the
detections plot on the top and the occupancy plot on the bottom so we
will recreate these for this year using ggarrange().
Unfortunately I could not figure out how to do this in purrr to reduce coding but luckily it isn’t too much repetition
# not sure I know how to do the following section in purrr just yet, but we've saved a ton of coding so far and it doesn't take much to arrange each of these individually
# LU2
LU02_det_occ_plots <- ggarrange(detection_plots$`Detections LU02`,
occupancy_plots$`Naive Occupancy LU02`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU02_det_occ_plots
# LU3
LU03_det_occ_plots <- ggarrange(detection_plots$`Detections LU03`,
occupancy_plots$`Naive Occupancy LU03`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU03_det_occ_plots
# LU1
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU01_det_occ_plots <- ggarrange(detection_plots$`Detections LU01`,
occupancy_plots$`Naive Occupancy LU01`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU01_det_occ_plots
# LU13
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU13_det_occ_plots <- ggarrange(detection_plots$`Detections LU13`,
occupancy_plots$`Naive Occupancy LU13`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU13_det_occ_plots
# LU15
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU15_det_occ_plots <- ggarrange(detection_plots$`Detections LU15`,
occupancy_plots$`Naive Occupancy LU15`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU15_det_occ_plots
# LU21
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU21_det_occ_plots <- ggarrange(detection_plots$`Detections LU21`,
occupancy_plots$`Naive Occupancy LU21`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU21_det_occ_plots
We can however, save all the figures again using purrr
# save all figures at once using purrr
final_det_occ_plots <- list(LU02_det_occ_plots,
LU03_det_occ_plots,
LU01_det_occ_plots,
LU13_det_occ_plots,
LU15_det_occ_plots,
LU21_det_occ_plots) %>%
purrr::set_names('LU02_det_occ_plots',
'LU03_det_occ_plots',
'LU01_det_occ_plots',
'LU13_det_occ_plots',
'LU15_det_occ_plots',
'LU21_det_occ_plots') %>%
purrr::imap(
~ggsave(.x,
file = paste0("figures/OSM_",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 12,
height = 15,
units = 'in'))Finish with detection data
Before proceeding let’s clear the objects currently in our environment since we don’t need them for the analysis
rm(list = ls(all.names = TRUE))Global Analysis prep
Now we can start the analysis prep.
First we need to read in the proportional detection (response metrics) and covariate (explanatory metrics) data files for all 6 LUs (fiscal years 2021-2022 and 2022-2023)
Response metrics
Read in data files
We haven’t joined the response metrics from 2021-2022 and 2022-2023 in any previous scripts yet, so let’s read those in using purrr and then join them and take a look at the data to make sure it looks good.
# response metric (proportional detections from the from the ACME_camera_script_9-2-2024.R or .Rmd)
prop_detections <- file.path('data/processed',
c('OSM_proportional_detections_2021.csv',
'OSM_proportional_detections_2022.csv')) %>%
map(~.x %>%
read_csv(.,
# set the column types to read in correctly
col_types = cols(site = col_factor(),
.default = col_number()))) %>%
# give names to each data frame in list
purrr::set_names('prop_dets_2021',
'prop_dets_2022') # R doesn't like when they are just numbers, you can make it work but it's annoying to call the data frame later so I've called them prop_dets_year
# check variable structure
str(prop_detections)## List of 2
## $ prop_dets_2021: spc_tbl_ [78 × 23] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## ..$ site : Factor w/ 78 levels "LU2_03","LU2_05",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ black_bear : num [1:78] 2 2 1 3 2 3 4 3 1 2 ...
## ..$ coyote : num [1:78] 1 0 3 5 6 3 8 0 3 4 ...
## ..$ fisher : num [1:78] 2 0 3 1 0 2 1 1 1 0 ...
## ..$ snowshoe_hare : num [1:78] 1 0 2 5 6 9 14 0 7 3 ...
## ..$ white-tailed_deer : num [1:78] 7 3 6 7 6 7 9 6 5 13 ...
## ..$ cougar : num [1:78] 0 1 0 0 0 0 1 0 0 1 ...
## ..$ lynx : num [1:78] 0 0 3 2 0 4 6 0 1 0 ...
## ..$ red_fox : num [1:78] 0 0 3 0 0 0 0 1 0 0 ...
## ..$ moose : num [1:78] 0 0 0 0 1 0 3 3 0 1 ...
## ..$ grey_wolf : num [1:78] 0 0 0 0 0 0 0 0 1 0 ...
## ..$ caribou : num [1:78] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ absent_black_bear : num [1:78] 3 3 4 2 3 8 7 2 4 9 ...
## ..$ absent_coyote : num [1:78] 6 7 4 2 1 11 6 7 4 10 ...
## ..$ absent_fisher : num [1:78] 5 7 4 6 7 12 13 6 6 14 ...
## ..$ absent_snowshoe_hare : num [1:78] 6 7 5 2 1 5 0 7 0 11 ...
## ..$ absent_white-tailed_deer: num [1:78] 0 4 1 0 1 7 5 1 2 1 ...
## ..$ absent_cougar : num [1:78] 7 6 7 7 7 14 13 7 7 13 ...
## ..$ absent_lynx : num [1:78] 7 7 4 5 7 10 8 7 6 14 ...
## ..$ absent_red_fox : num [1:78] 7 7 4 7 7 14 14 6 7 14 ...
## ..$ absent_moose : num [1:78] 7 7 7 7 6 14 11 4 7 13 ...
## ..$ absent_grey_wolf : num [1:78] 7 7 7 7 7 14 14 7 6 14 ...
## ..$ absent_caribou : num [1:78] 7 7 7 7 7 14 14 7 7 14 ...
## ..- attr(*, "spec")=
## .. .. cols(
## .. .. .default = col_number(),
## .. .. site = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. .. black_bear = col_number(),
## .. .. coyote = col_number(),
## .. .. fisher = col_number(),
## .. .. snowshoe_hare = col_number(),
## .. .. `white-tailed_deer` = col_number(),
## .. .. cougar = col_number(),
## .. .. lynx = col_number(),
## .. .. red_fox = col_number(),
## .. .. moose = col_number(),
## .. .. grey_wolf = col_number(),
## .. .. caribou = col_number(),
## .. .. absent_black_bear = col_number(),
## .. .. absent_coyote = col_number(),
## .. .. absent_fisher = col_number(),
## .. .. absent_snowshoe_hare = col_number(),
## .. .. `absent_white-tailed_deer` = col_number(),
## .. .. absent_cougar = col_number(),
## .. .. absent_lynx = col_number(),
## .. .. absent_red_fox = col_number(),
## .. .. absent_moose = col_number(),
## .. .. absent_grey_wolf = col_number(),
## .. .. absent_caribou = col_number()
## .. .. )
## ..- attr(*, "problems")=<externalptr>
## $ prop_dets_2022: spc_tbl_ [154 × 27] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## ..$ site : Factor w/ 154 levels "LU01_06","LU01_10",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ black_bear : num [1:154] 7 3 4 7 8 9 4 5 7 7 ...
## ..$ coyote : num [1:154] 4 4 8 10 11 9 11 0 9 4 ...
## ..$ fisher : num [1:154] 4 3 3 3 2 1 1 2 0 3 ...
## ..$ moose : num [1:154] 3 2 5 9 1 0 2 4 1 0 ...
## ..$ red_squirrel : num [1:154] 3 0 8 0 10 1 15 0 9 3 ...
## ..$ snowshoe_hare : num [1:154] 4 1 3 0 8 2 2 0 12 4 ...
## ..$ white-tailed_deer : num [1:154] 12 5 12 12 13 14 15 9 12 10 ...
## ..$ cougar : num [1:154] 0 0 1 0 1 0 0 0 0 0 ...
## ..$ grey_wolf : num [1:154] 0 0 2 0 0 0 1 0 0 0 ...
## ..$ lynx : num [1:154] 0 0 1 0 1 1 0 0 0 2 ...
## ..$ red_fox : num [1:154] 0 0 2 0 0 0 0 0 4 0 ...
## ..$ wolverine : num [1:154] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ caribou : num [1:154] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ absent_black_bear : num [1:154] 5 3 8 5 4 3 8 7 5 5 ...
## ..$ absent_coyote : num [1:154] 10 1 6 5 3 5 4 15 6 11 ...
## ..$ absent_fisher : num [1:154] 10 2 11 12 12 13 14 13 15 12 ...
## ..$ absent_moose : num [1:154] 11 3 9 6 13 14 13 11 14 15 ...
## ..$ absent_red_squirrel : num [1:154] 11 5 6 15 4 13 0 15 6 12 ...
## ..$ absent_snowshoe_hare : num [1:154] 10 4 11 15 6 12 13 15 3 11 ...
## ..$ absent_white-tailed_deer: num [1:154] 2 0 2 3 1 0 0 6 3 5 ...
## ..$ absent_cougar : num [1:154] 14 5 13 15 13 14 15 15 15 15 ...
## ..$ absent_grey_wolf : num [1:154] 14 5 12 15 14 14 14 15 15 15 ...
## ..$ absent_lynx : num [1:154] 14 5 13 15 13 13 15 15 15 13 ...
## ..$ absent_red_fox : num [1:154] 14 5 12 15 14 14 15 15 11 15 ...
## ..$ absent_wolverine : num [1:154] 14 5 14 15 14 14 15 15 15 15 ...
## ..$ absent_caribou : num [1:154] 14 5 14 15 14 14 15 15 15 15 ...
## ..- attr(*, "spec")=
## .. .. cols(
## .. .. .default = col_number(),
## .. .. site = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. .. black_bear = col_number(),
## .. .. coyote = col_number(),
## .. .. fisher = col_number(),
## .. .. moose = col_number(),
## .. .. red_squirrel = col_number(),
## .. .. snowshoe_hare = col_number(),
## .. .. `white-tailed_deer` = col_number(),
## .. .. cougar = col_number(),
## .. .. grey_wolf = col_number(),
## .. .. lynx = col_number(),
## .. .. red_fox = col_number(),
## .. .. wolverine = col_number(),
## .. .. caribou = col_number(),
## .. .. absent_black_bear = col_number(),
## .. .. absent_coyote = col_number(),
## .. .. absent_fisher = col_number(),
## .. .. absent_moose = col_number(),
## .. .. absent_red_squirrel = col_number(),
## .. .. absent_snowshoe_hare = col_number(),
## .. .. `absent_white-tailed_deer` = col_number(),
## .. .. absent_cougar = col_number(),
## .. .. absent_grey_wolf = col_number(),
## .. .. absent_lynx = col_number(),
## .. .. absent_red_fox = col_number(),
## .. .. absent_wolverine = col_number(),
## .. .. absent_caribou = col_number()
## .. .. )
## ..- attr(*, "problems")=<externalptr>Merge data files
Now we need to merge the two data files for analysis. We can do this with dplyr.
# merge the proportional detections files so there are rows for both fiscal years
prop_dets_all <- dplyr::bind_rows(prop_detections$prop_dets_2021,
prop_detections$prop_dets_2022)
print(prop_dets_all)## # A tibble: 232 × 27
## site black_bear coyote fisher snowshoe_hare `white-tailed_deer` cougar lynx
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LU2_… 2 1 2 1 7 0 0
## 2 LU2_… 2 0 0 0 3 1 0
## 3 LU2_… 1 3 3 2 6 0 3
## 4 LU2_… 3 5 1 5 7 0 2
## 5 LU2_… 2 6 0 6 6 0 0
## 6 LU2_… 3 3 2 9 7 0 4
## 7 LU2_… 4 8 1 14 9 1 6
## 8 LU2_… 3 0 1 0 6 0 0
## 9 LU2_… 1 3 1 7 5 0 1
## 10 LU2_… 2 4 0 3 13 1 0
## # ℹ 222 more rows
## # ℹ 19 more variables: red_fox <dbl>, moose <dbl>, grey_wolf <dbl>,
## # caribou <dbl>, absent_black_bear <dbl>, absent_coyote <dbl>,
## # absent_fisher <dbl>, absent_snowshoe_hare <dbl>,
## # `absent_white-tailed_deer` <dbl>, absent_cougar <dbl>, absent_lynx <dbl>,
## # absent_red_fox <dbl>, absent_moose <dbl>, absent_grey_wolf <dbl>,
## # absent_caribou <dbl>, red_squirrel <dbl>, wolverine <dbl>, …Format data
This looks good except since there were no wolverines in the first fiscal year of monitoring (LU02 and LU03) those columns have NAs for both arrays, we want to replace those NAs with Zeros and move the wolverine column to the correct location
Let’s do that now.
prop_dets_all <- prop_dets_all %>%
# replace NAs introduced from joining data to zeros
replace(is.na(.),
0) %>%
# relocate wolverine column
relocate(.,
wolverine,
.after = caribou)
# check data
head(prop_dets_all)## # A tibble: 6 × 27
## site black_bear coyote fisher snowshoe_hare `white-tailed_deer` cougar lynx
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LU2_03 2 1 2 1 7 0 0
## 2 LU2_05 2 0 0 0 3 1 0
## 3 LU2_1… 1 3 3 2 6 0 3
## 4 LU2_1… 3 5 1 5 7 0 2
## 5 LU2_1… 2 6 0 6 6 0 0
## 6 LU2_1… 3 3 2 9 7 0 4
## # ℹ 19 more variables: red_fox <dbl>, moose <dbl>, grey_wolf <dbl>,
## # caribou <dbl>, wolverine <dbl>, absent_black_bear <dbl>,
## # absent_coyote <dbl>, absent_fisher <dbl>, absent_snowshoe_hare <dbl>,
## # `absent_white-tailed_deer` <dbl>, absent_cougar <dbl>, absent_lynx <dbl>,
## # absent_red_fox <dbl>, absent_moose <dbl>, absent_grey_wolf <dbl>,
## # absent_caribou <dbl>, red_squirrel <dbl>, absent_red_squirrel <dbl>,
## # absent_wolverine <dbl>Looks good!
Save data
Let’s save the merged and formatted detection data from 2021 and 2022 for future use
write_csv(prop_dets_all,
'data/processed/OSM_proportional_detections_merged_2021_2022.csv')Covariates
In the previous script, 2_ACME_landscape_covariate_exploration_script.Rmd we joined the two fiscal years of data and grouped the covariates for analysis and saved this data as a csv file, so we can read in this file now and we shouldn’t have to do any further formatting at the moment
Read data
We will check the data structure after reading in the file just to make sure everything looks good.
covariates_all <- read_csv('data/processed/OSM_covariates_grouped_2021_2022.csv',
# set the column types to read in correctly
col_types = cols(array = col_factor(),
camera = col_factor(),
site = col_factor(),
buff_dist = col_factor(),
.default = col_number()))## Warning: The following named parsers don't match the column names: camerastr(covariates_all)## spc_tbl_ [4,660 × 19] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ array : Factor w/ 6 levels "LU13","LU15",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 233 levels "LU13_18","LU13_15",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ buff_dist : Factor w/ 20 levels "250","500","750",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ harvest : num [1:4660] 0 0 0.687 0.337 0 ...
## $ pipeline : num [1:4660] 0 0.068 0 0 0.0301 ...
## $ roads : num [1:4660] 0 0.0174 0 0 0 ...
## $ seismic_lines : num [1:4660] 0 0.03277 0 0.00889 0.01144 ...
## $ seismic_lines_3D : num [1:4660] 0 0 0 0 0.0523 ...
## $ trails : num [1:4660] 0.00588 0.0028 0 0.01591 0 ...
## $ transmission_lines: num [1:4660] 0.0642 0 0 0 0.091 ...
## $ veg_edges : num [1:4660] 0 0.0858 0 0 0 ...
## $ wells : num [1:4660] 0 0 0 0 0.0322 ...
## $ lc_grassland : num [1:4660] 0.193 0.348 0 0 0.178 ...
## $ lc_coniferous : num [1:4660] 0.456 0.358 0.186 1 0.822 ...
## $ lc_broadleaf : num [1:4660] 0 0 0 0 0 ...
## $ lc_mixed : num [1:4660] 0 0.101 0.255 0 0 ...
## $ lc_developed : num [1:4660] 0 0.0916 0 0 0 ...
## $ lc_shrub : num [1:4660] 0.316 0 0.559 0 0 ...
## $ osm_industrial : num [1:4660] 0.383 0.157 0 0 0 ...
## - attr(*, "spec")=
## .. cols(
## .. .default = col_number(),
## .. array = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. site = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. buff_dist = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. harvest = col_number(),
## .. pipeline = col_number(),
## .. roads = col_number(),
## .. seismic_lines = col_number(),
## .. seismic_lines_3D = col_number(),
## .. trails = col_number(),
## .. transmission_lines = col_number(),
## .. veg_edges = col_number(),
## .. wells = col_number(),
## .. lc_grassland = col_number(),
## .. lc_coniferous = col_number(),
## .. lc_broadleaf = col_number(),
## .. lc_mixed = col_number(),
## .. lc_developed = col_number(),
## .. lc_shrub = col_number(),
## .. osm_industrial = col_number()
## .. )
## - attr(*, "problems")=<externalptr>Everything looks good!
###Subset data by buffer
We do need to subset the data so we have separate data frames for each buffer width to work with in the analysis AND to explore correlations between variables at each buffer width, as these may very with spatial scales
Let’s use a for loop to subset the data, thanks Andrew!
buffer_frames <- list()
for (i in unique(covariates_all$buff_dist)){
print(i)
# Subset data based on radius
df <- covariates_all %>%
filter(buff_dist == i)
# list of dataframes
buffer_frames <-c (buffer_frames, list(df))
}## [1] "250"
## [1] "500"
## [1] "750"
## [1] "1000"
## [1] "1250"
## [1] "1500"
## [1] "1750"
## [1] "2000"
## [1] "2250"
## [1] "2500"
## [1] "2750"
## [1] "3000"
## [1] "3250"
## [1] "3500"
## [1] "3750"
## [1] "4000"
## [1] "4250"
## [1] "4500"
## [1] "4750"
## [1] "5000"# name list objects so we can extract names for plotting
buffer_frames <- buffer_frames %>%
# absurdly long way to do this but for sake of time fuck it
purrr::set_names('250 meter buffer',
'500 meter buffer',
'750 meter buffer',
'1000 meter buffer',
'1250 meter buffer',
'1500 meter buffer',
'1750 meter buffer',
'2000 meter buffer',
'2250 meter buffer',
'2500 meter buffer',
'2750 meter buffer',
'3000 meter buffer',
'3250 meter buffer',
'3500 meter buffer',
'3750 meter buffer',
'4000 meter buffer',
'4250 meter buffer',
'4500 meter buffer',
'4750 meter buffer',
'5000 meter buffer')Now we have a list with data frames for each buffer width which we can work with later.
Add response metric
Now that we have the covariate data formatted we need to add the response metric (monthly proportional presence/absence) to the data frames
osm_final_df_2021_2022 <- buffer_frames %>%
purrr::map(
~.x %>%
left_join(prop_dets_all,
by = 'site'))Finish with data formatting
Let’s remove the objects we no longer need from the environment to keep our work space clean
rm(covariates_all,
prop_detections,
df,
i)Global Analysis
Now we are going to run a global model which includes all HFI and LC variables that at first glance (will do a more thorough check later) seem to have enough data to include as covariates for each buffer width, and then we will compare these models see which buffer width best fit the data for each species. After that we will optimize models so they don’t includes any variables that are highly correlated.
Edit: we added subset analsyis (anthropogenic and landscape) where we subset the data into anthropogenic features (HFI) and landscape (landcover classes) to see how optimum buffer size was influenced based on the type of variables included in the models. For these subset analyses we also looked more closely at which variables were correlated and have adjusted the global models accordingly (i.e. dropped/merged the same variables) to make them more accurate and comparable to the subset models. If there are variables commented out of the models, these were used in the initial run-through of the analysis but were dropped in the subset models so I’ve dropped them accordingly in a re-run of the global models.
This almost means we have to add one quick data formatting step which wasn’t here in the initial run through of the data
osm_final_df_2021_2022 <- osm_final_df_2021_2022 %>%
# use purr so all changes are made to all data frames
purrr::map(
~.x %>%
# mutate data to merge variables that were correlated and similar enough to count as one for the purposes of this analysis (full explanation in subset analysis prep section)
mutate(pipeline_transmission_lines = pipeline + transmission_lines,
lc_forest = lc_coniferous + lc_broadleaf + lc_mixed))
# view structure of one data frame
str(osm_final_df_2021_2022$`250 meter buffer`)## tibble [233 × 47] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 6 levels "LU13","LU15",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 233 levels "LU13_18","LU13_15",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ buff_dist : Factor w/ 20 levels "250","500","750",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ harvest : num [1:233] 0 0 0.687 0.337 0 ...
## $ pipeline : num [1:233] 0 0.068 0 0 0.0301 ...
## $ roads : num [1:233] 0 0.0174 0 0 0 ...
## $ seismic_lines : num [1:233] 0 0.03277 0 0.00889 0.01144 ...
## $ seismic_lines_3D : num [1:233] 0 0 0 0 0.0523 ...
## $ trails : num [1:233] 0.00588 0.0028 0 0.01591 0 ...
## $ transmission_lines : num [1:233] 0.0642 0 0 0 0.091 ...
## $ veg_edges : num [1:233] 0 0.0858 0 0 0 ...
## $ wells : num [1:233] 0 0 0 0 0.0322 ...
## $ lc_grassland : num [1:233] 0.193 0.348 0 0 0.178 ...
## $ lc_coniferous : num [1:233] 0.456 0.358 0.186 1 0.822 ...
## $ lc_broadleaf : num [1:233] 0 0 0 0 0 ...
## $ lc_mixed : num [1:233] 0 0.101 0.255 0 0 ...
## $ lc_developed : num [1:233] 0 0.0916 0 0 0 ...
## $ lc_shrub : num [1:233] 0.316 0 0.559 0 0 ...
## $ osm_industrial : num [1:233] 0.383 0.157 0 0 0 ...
## $ black_bear : num [1:233] 1 4 6 2 0 3 1 2 1 1 ...
## $ coyote : num [1:233] 9 8 5 3 2 7 9 0 0 1 ...
## $ fisher : num [1:233] 0 0 0 1 0 0 0 0 0 0 ...
## $ snowshoe_hare : num [1:233] 11 9 0 0 2 3 8 3 0 3 ...
## $ white-tailed_deer : num [1:233] 4 5 3 0 0 3 4 0 0 0 ...
## $ cougar : num [1:233] 0 0 0 0 0 0 0 0 0 0 ...
## $ lynx : num [1:233] 4 1 0 0 1 4 1 1 0 2 ...
## $ red_fox : num [1:233] 0 0 0 0 0 0 0 0 0 0 ...
## $ moose : num [1:233] 0 0 4 0 1 1 0 1 0 3 ...
## $ grey_wolf : num [1:233] 0 3 0 3 0 0 0 0 0 0 ...
## $ caribou : num [1:233] 0 0 0 0 0 0 0 0 0 0 ...
## $ wolverine : num [1:233] 0 0 0 0 0 0 0 0 0 0 ...
## $ absent_black_bear : num [1:233] 8 5 3 7 9 6 8 7 8 8 ...
## $ absent_coyote : num [1:233] 3 4 7 9 10 5 3 12 12 11 ...
## $ absent_fisher : num [1:233] 12 12 12 11 12 12 12 12 12 12 ...
## $ absent_snowshoe_hare : num [1:233] 1 3 12 12 10 9 4 9 12 9 ...
## $ absent_white-tailed_deer : num [1:233] 8 7 9 12 12 9 8 12 12 12 ...
## $ absent_cougar : num [1:233] 12 12 12 12 12 12 12 12 12 12 ...
## $ absent_lynx : num [1:233] 8 11 12 12 11 8 11 11 12 10 ...
## $ absent_red_fox : num [1:233] 12 12 12 12 12 12 12 12 12 12 ...
## $ absent_moose : num [1:233] 12 12 8 12 11 11 12 11 12 9 ...
## $ absent_grey_wolf : num [1:233] 12 9 12 9 12 12 12 12 12 12 ...
## $ absent_caribou : num [1:233] 12 12 12 12 12 12 12 12 12 12 ...
## $ red_squirrel : num [1:233] 10 3 0 7 0 6 5 1 0 0 ...
## $ absent_red_squirrel : num [1:233] 2 9 12 5 12 6 7 11 12 12 ...
## $ absent_wolverine : num [1:233] 12 12 12 12 12 12 12 12 12 12 ...
## $ pipeline_transmission_lines: num [1:233] 0.0642 0.068 0 0 0.1211 ...
## $ lc_forest : num [1:233] 0.456 0.459 0.441 1 0.822 ...We don’t need to do ALL the species since many don’t have enough data.
Refer to the 1_ACME_camera_script_9-2-2024.html or .Rmd the plot for proportional monthly detections should provide info on which species we have enough data for, can be found under Response metrics/3.Proportion monthly detections
A brief look at this fig indicates that we have enough for all the mammals in the prop_detections data frame except
- cougar
- wolverine
- caribou??? (may have enough, may not)
Black bear
there is probably a way to shorten the following code to select particular species, I saw Andrew’s for loop in the draft script he wrote but couldn’t quite figure out how to adapt it to my purposes with the data formatted the way I have it, so I did this instead, maybe we can merge approaches later to clean this up if deemed necessary? But it certainly functions for now and is understandable… I think.
Global models
Let’s start with bears and use purrr to create a global model for every buffer distance
Recall purrr::map() is magical for iterations and will
apply all the functions within the map() function to each
item of the list supplied before the the map()
function.
# create models for black bears at each buffer size
black_bear_mods <- osm_final_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI
scale(harvest) +
# scale(pipeline) +
# scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
# scale(transmission_lines) +
# scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# VEG covariates in numerical order
scale(lc_grassland) +
# scale(lc_coniferous) +
# scale(lc_broadleaf) +
# scale(lc_mixed) +
scale(lc_developed) +
# scale(lc_shrub) +
scale(lc_forest) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))Model selection
We will use the model.sel() function from the
MuMIn package to compare the global models for each buffer
width and see which buffer fits the black bear data best
# run model selection and save the results as a tibble for graphing use later
black_bear_global_model.sel <- model.sel(black_bear_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_final_df_2021_2022)))
# look at model selection results
black_bear_global_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 250 meter buffer -0.5980169 + 0.007994002
## 2 3750 meter buffer -0.5985159 + 0.063737243
## 3 4000 meter buffer -0.5988958 + 0.066912736
## 4 3500 meter buffer -0.5983175 + 0.055592270
## 5 3250 meter buffer -0.5979959 + 0.057684143
## 6 4250 meter buffer -0.5985903 + 0.074157040
## 7 2750 meter buffer -0.5964813 + 0.051593393
## 8 3000 meter buffer -0.5969842 + 0.054352443
## 9 2500 meter buffer -0.5953182 + 0.043643124
## 10 4500 meter buffer -0.5981399 + 0.084455835
## 11 2250 meter buffer -0.5949003 + 0.034920531
## 12 2000 meter buffer -0.5957579 + 0.035769335
## 13 1750 meter buffer -0.5965510 + 0.037729143
## 14 1500 meter buffer -0.5968317 + 0.038047627
## 15 4750 meter buffer -0.5982737 + 0.084943338
## 16 5000 meter buffer -0.5977571 + 0.089220581
## 17 1250 meter buffer -0.5961642 + 0.028780877
## 18 1000 meter buffer -0.5953469 + 0.019342371
## 19 500 meter buffer -0.5967796 + 0.032865831
## 20 750 meter buffer -0.5945338 + 0.031272604
## cond(scale(lc_developed)) cond(scale(lc_forest)) cond(scale(lc_grassland))
## 1 -0.18817816 -0.117329550 -0.001633920
## 2 -0.12238956 -0.018577823 -0.033644857
## 3 -0.11741932 -0.019160942 -0.021771808
## 4 -0.11954337 -0.004563477 -0.027074375
## 5 -0.11757691 0.005321373 -0.027887991
## 6 -0.11001361 -0.030798057 -0.017549031
## 7 -0.12577734 0.024428922 -0.039484929
## 8 -0.11949519 0.016129793 -0.035466584
## 9 -0.12320851 0.028731402 -0.060955671
## 10 -0.09582794 -0.041341629 -0.022064400
## 11 -0.12626177 0.039872849 -0.065079993
## 12 -0.12271892 0.041853052 -0.051017515
## 13 -0.12031961 0.031947677 -0.038185093
## 14 -0.13837424 0.016515660 -0.033476364
## 15 -0.08954523 -0.047390826 -0.027063637
## 16 -0.09662684 -0.058542128 -0.030799609
## 17 -0.13591024 0.015875842 -0.027751477
## 18 -0.10213770 0.036216575 -0.016250799
## 19 -0.11199818 -0.022960834 0.051657322
## 20 -0.10855727 0.028620814 0.007246516
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 0.033049532 -0.07254973
## 2 -0.110610223 -0.11564161
## 3 -0.110931416 -0.12731595
## 4 -0.093471225 -0.12514482
## 5 -0.080988433 -0.13400657
## 6 -0.110641347 -0.11108057
## 7 -0.049156036 -0.13673851
## 8 -0.064566331 -0.13648825
## 9 -0.038812955 -0.11762028
## 10 -0.117727387 -0.10938327
## 11 -0.024962780 -0.10897596
## 12 -0.009151511 -0.10615428
## 13 0.002659282 -0.10943393
## 14 0.017318953 -0.09956684
## 15 -0.117008201 -0.10327526
## 16 -0.116378237 -0.09421258
## 17 0.034924606 -0.08951920
## 18 0.013738406 -0.09475402
## 19 0.018231490 -0.12202041
## 20 0.030198632 -0.08703427
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.1168960 -0.01782996 0.12649868
## 2 -0.1216505 -0.12116452 0.10384016
## 3 -0.1160259 -0.12741813 0.10412186
## 4 -0.1136758 -0.10577448 0.09673126
## 5 -0.1098018 -0.09997384 0.09012450
## 6 -0.1191445 -0.14162573 0.10153308
## 7 -0.1125162 -0.09440463 0.07928799
## 8 -0.1103608 -0.09113884 0.07835177
## 9 -0.1117889 -0.09321001 0.08614513
## 10 -0.1253820 -0.15659265 0.10042965
## 11 -0.1122646 -0.09143361 0.09190455
## 12 -0.1146880 -0.10762045 0.08801546
## 13 -0.1198787 -0.12237009 0.07879741
## 14 -0.1286891 -0.10071781 0.07856426
## 15 -0.1283342 -0.15907323 0.09693842
## 16 -0.1358831 -0.16134235 0.09090227
## 17 -0.1341083 -0.06911243 0.08315494
## 18 -0.1301128 -0.05777813 0.08347821
## 19 -0.1183573 -0.02208990 0.07081656
## 20 -0.1314970 -0.03521406 0.04347846
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.01118399 12 -448.9609 923.3464 0.000000 0.678117929
## 2 0.28508132 12 -451.5357 928.4960 5.149550 0.051652889
## 3 0.28817305 12 -451.6143 928.6532 5.306750 0.047748422
## 4 0.26178688 12 -452.1408 929.7063 6.359888 0.028201561
## 5 0.26161862 12 -452.2350 929.8946 6.548196 0.025667433
## 6 0.27289486 12 -452.4707 930.3660 7.019633 0.020277352
## 7 0.25930355 12 -452.4741 930.3728 7.026364 0.020209226
## 8 0.25974061 12 -452.6879 930.8006 7.454142 0.016317699
## 9 0.24078162 12 -452.8100 931.0447 7.698294 0.014442482
## 10 0.28167893 12 -452.8168 931.0582 7.711804 0.014345258
## 11 0.23007384 12 -452.8612 931.1471 7.800727 0.013721416
## 12 0.19780467 12 -452.9433 931.3112 7.964820 0.012640568
## 13 0.17750600 12 -453.0539 931.5324 8.185981 0.011317280
## 14 0.16480949 12 -453.1899 931.8044 8.458003 0.009878095
## 15 0.27294373 12 -453.2413 931.9073 8.560848 0.009382978
## 16 0.27590212 12 -453.4016 932.2279 8.881446 0.007993258
## 17 0.13017251 12 -453.4538 932.3323 8.985866 0.007586634
## 18 0.11152605 12 -453.8759 933.1765 9.830099 0.004974236
## 19 0.08990772 12 -454.0540 933.5326 10.186168 0.004163006
## 20 0.12258499 12 -455.1710 935.7667 12.420327 0.001362277
## Dataset
## 1 osm_final_df_2021_2022
## 2 osm_final_df_2021_2022
## 3 osm_final_df_2021_2022
## 4 osm_final_df_2021_2022
## 5 osm_final_df_2021_2022
## 6 osm_final_df_2021_2022
## 7 osm_final_df_2021_2022
## 8 osm_final_df_2021_2022
## 9 osm_final_df_2021_2022
## 10 osm_final_df_2021_2022
## 11 osm_final_df_2021_2022
## 12 osm_final_df_2021_2022
## 13 osm_final_df_2021_2022
## 14 osm_final_df_2021_2022
## 15 osm_final_df_2021_2022
## 16 osm_final_df_2021_2022
## 17 osm_final_df_2021_2022
## 18 osm_final_df_2021_2022
## 19 osm_final_df_2021_2022
## 20 osm_final_df_2021_2022Looks like the smallest buffer (250) fits the data best for black bears.
Note about re-run global models: when we removed the correlated variables the top model satyed the same (250m) but the weight increased (from 0.159 to 0.678)
Let’s look at this model closer
Model summary
Since we aren’t interpreting the magnitude and direction of effect for individual variables in the model as you normally would, the model summary serves primarily to see that there are no issues with the top model (convergence issues, large SE, etc.) that could cause us to quetion the results of the model.sel() function.
summary(black_bear_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(black_bear, absent_black_bear) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## scale(lc_grassland) + scale(lc_developed) + scale(lc_forest) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 921.9 963.3 -449.0 897.9 220
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.08116 0.2849
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.598017 0.125793 -4.754 1.99e-06 ***
## scale(harvest) 0.007994 0.049036 0.163 0.87050
## scale(seismic_lines) -0.017830 0.052626 -0.339 0.73476
## scale(seismic_lines_3D) -0.116896 0.058268 -2.006 0.04484 *
## scale(trails) 0.126499 0.046332 2.730 0.00633 **
## scale(wells) -0.011184 0.053722 -0.208 0.83509
## scale(osm_industrial) 0.033050 0.051185 0.646 0.51848
## scale(pipeline_transmission_lines) -0.072550 0.063235 -1.147 0.25125
## scale(lc_grassland) -0.001634 0.059234 -0.028 0.97799
## scale(lc_developed) -0.188178 0.065107 -2.890 0.00385 **
## scale(lc_forest) -0.117330 0.070534 -1.663 0.09622 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Nothing looks fishy in the model summary for now, we will look at this more closely once we have a true top model.
Code to remove a model
At one point the 250 meter buffer was giving us issues so we had to remove it. After re-extracting the data and re-doing some data formatting this is no longer an issue but I’ve saved the code here in case it’s needed in the future
# create models for black bears at each buffer size
black_bear_mods_no250 <- osm_final_df_2021_2022 %>%
# remove 250 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to fun the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))Subset models
NOTE this code was not used for publication but was an initial idea for how we could further use the data. We only explored subset models for Bears thus far, but have left the headings for the other species in case we want to add in these subset models later
Autocorrelation 250m
Before we can develop model subsets we need to see what variables can be included in the same model at this buffer width.
Let’s use the chart.Correlation() function in the
Performance Analytics package to look at this.
buffer_frames$`250 meter buffer` %>%
select_if(is.numeric) %>%
# use chart.correlation
chart.Correlation(.,
histogram = TRUE,
method = "pearson")
mtext('250 meter buffer', side = 3, line = 3)
> You can click on this fig to zoom in!
List of correlated variables:
- pipeline & transmission_lines 0.53
- roads & veg_edges 0.71
- roads & lc_developed 0.57
Model list
Let’s create another global model without these correlated variables. I’m going to select transmission_lines over pipeline because the summary from earlier showed transmission lines had larger effect on black bear presence, and I’m going to choose to keep roads instead of veg edges and the developed landcover class because we are interested in the effect of roads more than these other two variables.
# global model w/ non-correlated variables
bear_global_250 <- glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI
scale(harvest) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = osm_final_df_2021_2022$`250 meter buffer`,
family = 'binomial')
# null model to compare
bear_null_250 <- glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~ 1 +
# Random effect of array
(1|array),
data = osm_final_df_2021_2022$`250 meter buffer`,
family = 'binomial')
# second model is based on linear features providing easier movement through boreal forest
bear_linear_250 <- glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
# Random effect of array
(1|array),
data = osm_final_df_2021_2022$`250 meter buffer`,
family = 'binomial')Model selection
Now let’s look at a model selection table with our subset models.
model.sel(bear_global_250,
bear_null_250,
bear_linear_250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## bear_linear_250 -0.5986 +
## bear_global_250 -0.5995 + 0.01223 0.2326
## bear_null_250 -0.5884 +
## cnd(scl(lc_cnf)) cnd(scl(lc_grs)) cnd(scl(lc_mxd))
## bear_linear_250
## bear_global_250 0.1823 0.1248 0.1455
## bear_null_250
## cnd(scl(lc_shr)) cnd(scl(osm_ind)) cnd(scl(rds))
## bear_linear_250 -0.1586
## bear_global_250 0.1891 0.04922 -0.1215
## bear_null_250
## cnd(scl(ssm_lns_3D)) cnd(scl(ssm_lns)) cnd(scl(trl))
## bear_linear_250 -0.1311 -0.04436 0.10250
## bear_global_250 -0.1135 -0.04271 0.09811
## bear_null_250
## cnd(scl(trn_lns)) cnd(scl(wll)) df logLik AICc delta weight
## bear_linear_250 -0.09901 7 -449.674 913.8 0.00 0.972
## bear_global_250 -0.10520 -0.01348 15 -444.383 921.0 7.14 0.027
## bear_null_250 2 -463.090 930.2 16.38 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)Looks like the linear feature model is best, SO FAR
Top model
250 meter buffer - linear features
Summary
summary(bear_linear_250)## Family: binomial ( logit )
## Formula:
## cbind(black_bear, absent_black_bear) ~ scale(roads) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(transmission_lines) +
## (1 | array)
## Data: osm_final_df_2021_2022$`250 meter buffer`
##
## AIC BIC logLik deviance df.resid
## 913.3 937.5 -449.7 899.3 225
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.05426 0.2329
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.59856 0.10644 -5.624 1.87e-08 ***
## scale(roads) -0.15864 0.05694 -2.786 0.00534 **
## scale(seismic_lines) -0.04436 0.05006 -0.886 0.37556
## scale(seismic_lines_3D) -0.13114 0.05635 -2.327 0.01995 *
## scale(trails) 0.10247 0.04641 2.208 0.02724 *
## scale(transmission_lines) -0.09901 0.05653 -1.751 0.07987 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Odds ratios
Let’s extract the odds ratios for the top model so we can plot them for data vis later.
bear_model_odds <- bear_linear_250 %>%
# extract the coefficients and upper and lower CI
confint() %>%
# format resulting object as a tibble data frame
as_tibble() %>%
# add a column where we can put the feature names
rowid_to_column() %>%
# rename the columns for plotting
rename('lower' = `2.5 %`,
'upper' = `97.5 %`,
'estimate' = Estimate,
'feature' = rowid) %>%
# rename the entries to features, need to look at the order the features are in from the model summary and ensure it matches
mutate(feature = as.factor(feature),
feature = recode(feature,
'1' = 'intercept',
'2' = 'roads',
'3' = 'seismic_lines',
'4' = 'seismic_lines_3D',
'5' = 'trails',
'6' = 'transmission_lines',
'7' = 'intercept_array')) %>%
# remove intercepts
filter(!grepl('intercept',
feature))Plot odds ratios
First let’s get a silhouette for this graphy from phylopic
black_bear_img <- get_phylopic(get_uuid(name = 'Ursus americanus'))Now let’s use ggplot to plot the odds ratios for each feature in the top model
# provide data and mapping aesthetics
ggplot(bear_model_odds, aes(x = feature,
y = estimate)) +
geom_errorbar(aes(ymin = lower,
ymax = upper),
width = 0.4,
linewidth = 0.5,
position = position_dodge(width = 0.9)) +
geom_hline(yintercept = 0, linetype = "dashed") +
labs(y = 'Odds ratio') +
scale_x_discrete(labels = c('Roads',
'Seismic Lines',
'3D Seismic Lines',
'Trails',
'Transmission Lines')) +
add_phylopic(black_bear_img,
x = 5.3,
y = 0.2,
ysize = 0.05) +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90,
hjust = 1),
axis.title = element_text(size = 16),
axis.text = element_text(size = 14))
Let’s repeat this process for each species that we have enough data for.
Caribou
We may or may not have enough data for caribou but let’s try it at least for this preliminary report
We can use the same code from black bears (above) to run global models for each buffer width
And in the same chunk to save time let’s also run the
model.sel() function
Global models
caribou_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(caribou, absent_caribou) ~
# HFI
scale(harvest) +
# scale(pipeline) +
# scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
# scale(transmission_lines) +
# scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# VEG covariates in numerical order
scale(lc_grassland) +
# scale(lc_coniferous) +
# scale(lc_broadleaf) +
# scale(lc_mixed) +
scale(lc_developed) +
# scale(lc_shrub) +
scale(lc_forest) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')# run model selection and save the results as a tibble for graphing use later
caribou_global_model.sel <- model.sel(caribou_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_final_df_2021_2022)))
# look at model selection results
caribou_global_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 1000 meter buffer -5.541691 + -0.65725167
## 2 1250 meter buffer -5.389313 + -0.36118018
## 3 750 meter buffer -5.765763 + -1.56407319
## 4 1500 meter buffer -5.302534 + -0.19770713
## 5 2500 meter buffer -5.603878 + -0.16267341
## 6 2000 meter buffer -5.379371 + -0.21428286
## 7 1750 meter buffer -5.282614 + -0.21810476
## 8 2750 meter buffer -5.573419 + -0.13425099
## 9 2250 meter buffer -5.475323 + -0.19149017
## 10 500 meter buffer -5.749800 + -2.30534167
## 11 3000 meter buffer -5.603490 + -0.04353968
## 12 250 meter buffer -204.202683 + -599.03806642
## 13 3250 meter buffer -5.702496 + 0.02383525
## 14 3500 meter buffer -5.674306 + -0.01945111
## 15 3750 meter buffer -5.640270 + -0.08865803
## 16 4750 meter buffer -5.192625 + -1.17614785
## 17 5000 meter buffer -5.222123 + -1.38062579
## 18 4000 meter buffer -5.527983 + -0.19871032
## 19 4500 meter buffer -5.133141 + -0.89512309
## 20 4250 meter buffer -5.195888 + -0.54075400
## cond(scale(lc_developed)) cond(scale(lc_forest)) cond(scale(lc_grassland))
## 1 0.06847734 0.205338004 -0.06587403
## 2 -0.09234293 0.190861812 -0.14997595
## 3 -0.04325940 0.253684235 -0.06969995
## 4 -0.30868340 0.207730850 -0.24014413
## 5 0.32758898 0.176629676 -0.34144706
## 6 0.10330649 0.192238820 -0.27845455
## 7 -0.31571093 0.180899772 -0.26739770
## 8 0.30491896 0.140374009 -0.31461343
## 9 0.24039981 0.191954202 -0.34027327
## 10 -0.05809455 0.159390735 -0.13692140
## 11 0.44238834 0.098769628 -0.25212109
## 12 -0.30358218 0.177228128 -0.14613326
## 13 0.61272256 0.062340559 -0.24326333
## 14 0.62165873 0.046119302 -0.26295003
## 15 0.62134322 0.033176341 -0.28878257
## 16 -0.59518026 -0.072105246 -0.38774229
## 17 -0.65944116 -0.094553901 -0.38136360
## 18 0.51387299 0.006902339 -0.31771379
## 19 -0.25990904 -0.032875304 -0.36765369
## 20 0.02969350 -0.015897931 -0.33420234
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 -1.2929550 -0.3002017
## 2 -1.1419093 -0.2206548
## 3 -1.0232651 -0.2924596
## 4 -0.9155964 -0.2617808
## 5 -1.4878903 -0.7149004
## 6 -1.5029489 -0.4076253
## 7 -1.0907323 -0.2710183
## 8 -1.1069871 -0.8976197
## 9 -1.6242628 -0.4375504
## 10 -0.3164276 -0.1656729
## 11 -0.9702550 -0.9864143
## 12 -0.1913004 -0.2072504
## 13 -0.9267209 -0.9570913
## 14 -0.8173317 -0.9677301
## 15 -0.7580180 -0.9200752
## 16 0.0774661 -0.6926807
## 17 0.0916440 -0.6611871
## 18 -0.6265348 -0.8016489
## 19 -0.0755507 -0.7022723
## 20 -0.2868306 -0.7415273
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.1386450 0.143810555 0.04559153
## 2 -0.1474092 0.176342455 0.05588960
## 3 -0.1412665 0.219454946 0.03604664
## 4 -0.2001540 0.086853722 -0.04956552
## 5 -0.1131389 0.163996802 -0.19603058
## 6 -0.1097337 0.104234593 -0.06962252
## 7 -0.1938458 0.022045525 -0.04380021
## 8 -0.1277863 0.115521182 -0.26147205
## 9 -0.1162640 0.176184157 -0.09751303
## 10 -0.1700043 0.209347341 -0.04656235
## 11 -0.1276584 0.009939888 -0.37206550
## 12 -0.2941479 0.229333639 -0.29452119
## 13 -0.1511563 -0.071569637 -0.46886471
## 14 -0.1273545 -0.088015843 -0.50306198
## 15 -0.1116802 -0.116771313 -0.48833303
## 16 -0.1672512 0.051263313 -0.50796175
## 17 -0.1574435 0.078616191 -0.52941063
## 18 -0.1043717 -0.121080522 -0.42295765
## 19 -0.1232611 -0.011530570 -0.43142000
## 20 -0.1172785 -0.065326214 -0.41312655
## cond(scale(wells)) df logLik AICc delta weight
## 1 0.7342746 12 -131.8196 289.0640 0.000000 7.963951e-01
## 2 0.7962446 12 -134.1331 293.6908 4.626890 7.877930e-02
## 3 0.6896422 12 -134.2288 293.8823 4.818342 7.158779e-02
## 4 0.8722577 12 -135.5227 296.4701 7.406097 1.962976e-02
## 5 1.5625358 12 -136.3080 298.0406 8.976616 8.951200e-03
## 6 1.1628716 12 -136.6219 298.6685 9.604506 6.539380e-03
## 7 0.9260111 12 -136.9531 299.3310 10.266997 4.695463e-03
## 8 1.5513888 12 -136.9576 299.3399 10.275898 4.674613e-03
## 9 1.3500289 12 -136.9868 299.3982 10.334214 4.540280e-03
## 10 0.4975402 12 -137.7936 301.0118 11.947861 2.026206e-03
## 11 1.4900746 12 -138.5356 302.4958 13.431846 9.648066e-04
## 12 0.3695857 12 -138.8885 303.2016 14.137627 6.779253e-04
## 13 1.4380997 12 -140.0395 305.5037 16.439735 2.144300e-04
## 14 1.4153473 12 -140.5539 306.5325 17.468558 1.281975e-04
## 15 1.3861851 12 -141.1210 307.6667 18.602748 7.270984e-05
## 16 1.1279565 12 -141.7487 308.9221 19.858169 3.881343e-05
## 17 1.1220463 12 -141.7695 308.9636 19.899640 3.801690e-05
## 18 1.3090163 12 -142.3013 310.0273 20.963332 2.233567e-05
## 19 1.1030771 12 -142.8833 311.1913 22.127307 1.248087e-05
## 20 1.1761871 12 -142.9935 311.4116 22.347619 1.117905e-05
## Dataset
## 1 osm_final_df_2021_2022
## 2 osm_final_df_2021_2022
## 3 osm_final_df_2021_2022
## 4 osm_final_df_2021_2022
## 5 osm_final_df_2021_2022
## 6 osm_final_df_2021_2022
## 7 osm_final_df_2021_2022
## 8 osm_final_df_2021_2022
## 9 osm_final_df_2021_2022
## 10 osm_final_df_2021_2022
## 11 osm_final_df_2021_2022
## 12 osm_final_df_2021_2022
## 13 osm_final_df_2021_2022
## 14 osm_final_df_2021_2022
## 15 osm_final_df_2021_2022
## 16 osm_final_df_2021_2022
## 17 osm_final_df_2021_2022
## 18 osm_final_df_2021_2022
## 19 osm_final_df_2021_2022
## 20 osm_final_df_2021_2022We get a warning that there are some model convergence problems, I expect this is because we don’t have enough data for caribou, so we may choose to remove the caribou results from the manuscript or provide that caveat.
For now let’s examine the top buffer width and see if the model seems reasonable.
Note about re-run global models: when we re-ran the global models with dropped correlated variables our top model for caribou does change (3000m to 1000m) which now matches the top model for anthropogenic features, this could be because there are more anthro features in the global models and/or because of lack of data/model convergence issues. If we see this same trend with other global models it will be worth mentioning as a caveat in the discussion.
Model summary 1000m
Let’s take a closer look at the top model summary
summary(caribou_mods$`1000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(caribou, absent_caribou) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## scale(lc_grassland) + scale(lc_developed) + scale(lc_forest) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 287.6 329.0 -131.8 263.6 220
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 4.767 2.183
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.54169 1.07006 -5.179 2.23e-07 ***
## scale(harvest) -0.65725 0.64637 -1.017 0.30924
## scale(seismic_lines) 0.14381 0.16616 0.865 0.38678
## scale(seismic_lines_3D) -0.13865 0.21047 -0.659 0.51006
## scale(trails) 0.04559 0.20832 0.219 0.82676
## scale(wells) 0.73427 0.17137 4.285 1.83e-05 ***
## scale(osm_industrial) -1.29295 0.47653 -2.713 0.00666 **
## scale(pipeline_transmission_lines) -0.30020 0.30963 -0.970 0.33227
## scale(lc_grassland) -0.06587 0.16616 -0.396 0.69178
## scale(lc_developed) 0.06848 0.40830 0.168 0.86681
## scale(lc_forest) 0.20534 0.14382 1.428 0.15336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There’s nothing that catches my eye immediately as being sus about this particular model so it may not have been one with convergence issues. We will keep it in report for now
Subset Models
Add later if deemed necessary.
Coyote
Global models
coyote_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(coyote, absent_coyote) ~
# HFI
scale(harvest) +
# scale(pipeline) +
# scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
# scale(transmission_lines) +
# scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# VEG covariates in numerical order
scale(lc_grassland) +
# scale(lc_coniferous) +
# scale(lc_broadleaf) +
# scale(lc_mixed) +
scale(lc_developed) +
# scale(lc_shrub) +
scale(lc_forest) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
coyote_global_model.sel <- model.sel(coyote_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_final_df_2021_2022)))
# look at model selection results
coyote_global_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 5000 meter buffer -1.387405 + -0.084683809
## 2 4750 meter buffer -1.386435 + -0.080093043
## 3 4500 meter buffer -1.384919 + -0.070055963
## 4 4250 meter buffer -1.383120 + -0.061963914
## 5 1250 meter buffer -1.387643 + 0.041423041
## 6 1500 meter buffer -1.382727 + 0.040671391
## 7 2250 meter buffer -1.377760 + -0.011873066
## 8 1750 meter buffer -1.379182 + 0.026285665
## 9 2000 meter buffer -1.376632 + 0.003103946
## 10 3000 meter buffer -1.379774 + -0.024974850
## 11 2500 meter buffer -1.378784 + -0.016181448
## 12 2750 meter buffer -1.378749 + -0.021552916
## 13 4000 meter buffer -1.381507 + -0.059528492
## 14 1000 meter buffer -1.391280 + 0.042799808
## 15 3250 meter buffer -1.379660 + -0.031487002
## 16 3500 meter buffer -1.378673 + -0.043315893
## 17 3750 meter buffer -1.379494 + -0.049725718
## 18 750 meter buffer -1.392533 + 0.073423108
## 19 500 meter buffer -1.390117 + 0.038748362
## 20 250 meter buffer -1.386069 + -0.056699954
## cond(scale(lc_developed)) cond(scale(lc_forest)) cond(scale(lc_grassland))
## 1 0.3723829 -0.1599505 -0.201565893
## 2 0.3626378 -0.1687627 -0.197723406
## 3 0.3539336 -0.1750140 -0.192288881
## 4 0.3337514 -0.1807675 -0.171215491
## 5 0.3791189 -0.1453668 -0.022652122
## 6 0.3471607 -0.1458746 -0.033376423
## 7 0.3432047 -0.1490561 -0.092859888
## 8 0.3454284 -0.1416329 -0.053434037
## 9 0.3411187 -0.1375864 -0.073698581
## 10 0.3105397 -0.1719981 -0.060974060
## 11 0.3309993 -0.1559528 -0.077812366
## 12 0.3161171 -0.1623329 -0.060873299
## 13 0.3263633 -0.1865309 -0.140912758
## 14 0.3792161 -0.1430275 -0.011126504
## 15 0.3142354 -0.1785523 -0.076444084
## 16 0.3314600 -0.1843575 -0.107448755
## 17 0.3304539 -0.1920417 -0.127281904
## 18 0.3436510 -0.1184575 0.007329282
## 19 0.2172943 -0.1126626 0.020854512
## 20 0.1531684 -0.1636688 -0.022135648
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 0.3536675 0.1136902698
## 2 0.3539307 0.1075698503
## 3 0.3470543 0.0934759992
## 4 0.3402821 0.0789617270
## 5 0.2821016 0.0194312986
## 6 0.3035874 0.0311907903
## 7 0.3043412 0.0261521144
## 8 0.3033371 0.0253843467
## 9 0.3109182 0.0109109544
## 10 0.3340890 -0.0344420146
## 11 0.3135146 -0.0003666942
## 12 0.3360739 -0.0308855950
## 13 0.3177742 0.0469077523
## 14 0.2735007 0.0301858715
## 15 0.3277301 -0.0137064626
## 16 0.3026731 0.0135309452
## 17 0.2960900 0.0342291463
## 18 0.2877806 0.0875596873
## 19 0.2931698 0.0656488312
## 20 0.2168276 0.1311700564
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.07121340 0.2668503 -1.666319e-02
## 2 -0.07461374 0.2590864 -2.444740e-02
## 3 -0.07683031 0.2565248 -2.321689e-02
## 4 -0.07192316 0.2365325 -1.811958e-02
## 5 -0.14059392 0.2701879 1.330279e-02
## 6 -0.12764212 0.2595679 3.690687e-02
## 7 -0.08821219 0.2749065 6.001013e-02
## 8 -0.11131888 0.2521583 4.822123e-02
## 9 -0.09834766 0.2497815 4.571074e-02
## 10 -0.05023398 0.2206073 2.613415e-02
## 11 -0.07148100 0.2642255 4.629794e-02
## 12 -0.05897457 0.2322501 2.767566e-02
## 13 -0.06100372 0.2270799 -2.052174e-03
## 14 -0.13165027 0.2595744 -6.606805e-05
## 15 -0.04805539 0.2130992 1.474503e-02
## 16 -0.05270340 0.2203164 1.938682e-02
## 17 -0.05709709 0.2199482 1.475222e-02
## 18 -0.13414118 0.2508119 -3.534228e-02
## 19 -0.12495868 0.1928322 -7.749655e-02
## 20 -0.02725795 0.1533772 3.497855e-02
## cond(scale(wells)) df logLik AICc delta weight
## 1 0.00949774 12 -460.0687 945.5622 0.0000000 4.442975e-01
## 2 0.01628066 12 -460.2716 945.9678 0.4056534 3.627333e-01
## 3 0.03685148 12 -461.2274 947.8795 2.3173121 1.394684e-01
## 4 0.04561015 12 -463.7125 952.8496 7.2874875 1.162027e-02
## 5 0.09660289 12 -463.7166 952.8579 7.2957714 1.157224e-02
## 6 0.09859273 12 -464.0414 953.5074 7.9452171 8.363574e-03
## 7 0.09614335 12 -464.4395 954.3036 8.7414206 5.616924e-03
## 8 0.09473482 12 -464.7146 954.8538 9.2916655 4.265936e-03
## 9 0.09525575 12 -465.1718 955.7683 10.2061087 2.700509e-03
## 10 0.09630945 12 -465.4910 956.4067 10.8445222 1.962528e-03
## 11 0.09437106 12 -465.5461 956.5169 10.9547310 1.857309e-03
## 12 0.09051201 12 -465.9488 957.3223 11.7601157 1.241644e-03
## 13 0.05943287 12 -466.0326 957.4899 11.9277674 1.141805e-03
## 14 0.13733603 12 -466.0661 957.5568 11.9946770 1.104238e-03
## 15 0.08004487 12 -466.2721 957.9688 12.4066773 8.986657e-04
## 16 0.08138492 12 -466.7154 958.8555 13.2933960 5.768324e-04
## 17 0.07878630 12 -466.7895 959.0036 13.4414311 5.356785e-04
## 18 0.19147174 12 -469.3204 964.0655 18.5033626 4.263043e-05
## 19 0.20430702 12 -485.6540 996.7327 51.1705500 3.436609e-12
## 20 0.03063854 12 -503.9044 1033.2334 87.6712544 4.074768e-20
## Dataset
## 1 osm_final_df_2021_2022
## 2 osm_final_df_2021_2022
## 3 osm_final_df_2021_2022
## 4 osm_final_df_2021_2022
## 5 osm_final_df_2021_2022
## 6 osm_final_df_2021_2022
## 7 osm_final_df_2021_2022
## 8 osm_final_df_2021_2022
## 9 osm_final_df_2021_2022
## 10 osm_final_df_2021_2022
## 11 osm_final_df_2021_2022
## 12 osm_final_df_2021_2022
## 13 osm_final_df_2021_2022
## 14 osm_final_df_2021_2022
## 15 osm_final_df_2021_2022
## 16 osm_final_df_2021_2022
## 17 osm_final_df_2021_2022
## 18 osm_final_df_2021_2022
## 19 osm_final_df_2021_2022
## 20 osm_final_df_2021_2022Note about re-run global models: when we re-ran the global models with dropped correlated variables our top model for coyote did change (2250-5000). As with caribou this result is also more similar to the top anthro model (4750m)
Model summary 5000m
Let’s get the model summary for this model
summary(coyote_mods$`5000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(coyote, absent_coyote) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## scale(lc_grassland) + scale(lc_developed) + scale(lc_forest) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 944.1 985.5 -460.1 920.1 220
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.1189 0.3449
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.387405 0.150996 -9.188 < 2e-16 ***
## scale(harvest) -0.084684 0.094828 -0.893 0.371845
## scale(seismic_lines) 0.266850 0.077153 3.459 0.000543 ***
## scale(seismic_lines_3D) -0.071213 0.074950 -0.950 0.342037
## scale(trails) -0.016663 0.072397 -0.230 0.817965
## scale(wells) 0.009498 0.111273 0.085 0.931979
## scale(osm_industrial) 0.353667 0.064260 5.504 3.72e-08 ***
## scale(pipeline_transmission_lines) 0.113690 0.106001 1.073 0.283479
## scale(lc_grassland) -0.201566 0.091179 -2.211 0.027059 *
## scale(lc_developed) 0.372383 0.090102 4.133 3.58e-05 ***
## scale(lc_forest) -0.159951 0.070220 -2.278 0.022735 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Everything looks good here
Subset models
Add later if deemed necessary.
Fisher
Global models
fisher_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(fisher, absent_fisher) ~
# HFI
scale(harvest) +
# scale(pipeline) +
# scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
# scale(transmission_lines) +
# scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# VEG covariates in numerical order
scale(lc_grassland) +
# scale(lc_coniferous) +
# scale(lc_broadleaf) +
# scale(lc_mixed) +
scale(lc_developed) +
# scale(lc_shrub) +
scale(lc_forest) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
fisher_global_model.sel <- model.sel(fisher_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_final_df_2021_2022)))
# look at model selection results
fisher_global_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 1000 meter buffer -3.099759 + -0.2427972
## 2 750 meter buffer -3.078768 + -0.2584359
## 3 500 meter buffer -3.063430 + -0.3150145
## 4 2500 meter buffer -3.013959 + -0.1365859
## 5 1250 meter buffer -3.061498 + -0.2543493
## 6 2750 meter buffer -3.005513 + -0.1379150
## 7 2250 meter buffer -3.026091 + -0.1485053
## 8 3000 meter buffer -2.994056 + -0.1425289
## 9 3250 meter buffer -2.987314 + -0.1566246
## 10 2000 meter buffer -3.029025 + -0.1699645
## 11 3500 meter buffer -2.979809 + -0.1801562
## 12 1500 meter buffer -3.031835 + -0.2348054
## 13 4250 meter buffer -2.979184 + -0.2075828
## 14 1750 meter buffer -3.024476 + -0.2122054
## 15 4500 meter buffer -2.983539 + -0.2265939
## 16 4000 meter buffer -2.973866 + -0.1987231
## 17 3750 meter buffer -2.972995 + -0.1957554
## 18 4750 meter buffer -2.990213 + -0.2585855
## 19 5000 meter buffer -2.988975 + -0.2811262
## 20 250 meter buffer -3.001628 + -0.3662532
## cond(scale(lc_developed)) cond(scale(lc_forest)) cond(scale(lc_grassland))
## 1 0.5727364 0.7061622 0.21162467
## 2 0.5548374 0.7433866 0.22503536
## 3 0.5009800 0.6974368 0.26369034
## 4 0.5125109 0.5430848 -0.11199246
## 5 0.5113902 0.6093210 0.15921449
## 6 0.4877380 0.5311597 -0.16384457
## 7 0.5459942 0.5793126 -0.01138773
## 8 0.4720191 0.4993427 -0.21700795
## 9 0.4500998 0.4830171 -0.24140682
## 10 0.5262478 0.5935377 0.01940845
## 11 0.4491744 0.4816302 -0.21503001
## 12 0.4637941 0.5691423 0.10923414
## 13 0.4305839 0.4670011 -0.18053345
## 14 0.4536740 0.5800992 0.05581789
## 15 0.4128843 0.4763959 -0.15342957
## 16 0.4323847 0.4704532 -0.16090702
## 17 0.4323407 0.4689907 -0.17660125
## 18 0.3990150 0.4827507 -0.12926129
## 19 0.3666465 0.4737121 -0.10186128
## 20 0.2779568 0.4160728 0.25525233
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 -0.020861054 -0.5915789
## 2 -0.057031272 -0.4844852
## 3 -0.195261742 -0.3401411
## 4 0.031305000 -0.4934016
## 5 -0.044018282 -0.4645780
## 6 0.046908733 -0.4606777
## 7 0.037879489 -0.5188282
## 8 0.053226750 -0.3992715
## 9 0.001688767 -0.3516691
## 10 0.027615191 -0.4856637
## 11 -0.055968602 -0.3216140
## 12 -0.017429274 -0.3868388
## 13 -0.218543081 -0.3576092
## 14 0.029346500 -0.3844161
## 15 -0.266645809 -0.3674282
## 16 -0.139473041 -0.3664399
## 17 -0.102542445 -0.3284374
## 18 -0.325432645 -0.3376598
## 19 -0.327155081 -0.2853743
## 20 -0.243500329 -0.1912888
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.3745712 2.617337e-02 0.094363213
## 2 -0.3410204 1.612390e-02 0.065717590
## 3 -0.3199229 -5.498808e-02 0.060860337
## 4 -0.1680831 -8.696346e-02 -0.017205281
## 5 -0.3740559 -9.887550e-03 0.065767668
## 6 -0.1506485 -1.201066e-01 -0.035119085
## 7 -0.1975101 -4.348857e-02 -0.025004158
## 8 -0.1325913 -1.209405e-01 -0.046217325
## 9 -0.1280096 -1.117693e-01 -0.051492836
## 10 -0.2378094 -5.514954e-02 -0.022471592
## 11 -0.1314974 -8.438546e-02 -0.005697268
## 12 -0.3431068 -3.310497e-02 0.067488764
## 13 -0.1193407 -1.915035e-02 0.009935865
## 14 -0.3046761 -5.883176e-02 0.059906796
## 15 -0.1199883 -7.801534e-03 0.028683036
## 16 -0.1226651 -2.482316e-02 0.015423612
## 17 -0.1344931 -5.581889e-02 0.018250417
## 18 -0.1395798 6.659563e-03 0.067162756
## 19 -0.1712571 -9.998825e-05 0.085457257
## 20 -0.1936371 -7.432649e-02 0.008109032
## cond(scale(wells)) df logLik AICc delta weight
## 1 0.04743872 12 -269.7496 564.9238 0.000000 0.5256777832
## 2 0.06888444 12 -270.6270 566.6786 1.754847 0.2186047130
## 3 0.09495003 12 -271.3187 568.0620 3.138205 0.1094629257
## 4 0.18732329 12 -272.2356 569.8958 4.971987 0.0437589018
## 5 0.04406055 12 -272.6819 570.7884 5.864595 0.0280052152
## 6 0.21778468 12 -272.7780 570.9807 6.056933 0.0254374379
## 7 0.12024773 12 -273.3664 572.1576 7.233776 0.0141229401
## 8 0.22405348 12 -273.6817 572.7881 7.864355 0.0103037824
## 9 0.26657039 12 -273.8947 573.2141 8.290296 0.0083273102
## 10 0.10881300 12 -273.9343 573.2932 8.369465 0.0080041160
## 11 0.24999722 12 -275.3799 576.1845 11.260698 0.0018857757
## 12 0.03112278 12 -275.8137 577.0522 12.128376 0.0012220143
## 13 0.34950353 12 -275.9045 577.2336 12.309862 0.0011160072
## 14 0.06251461 12 -276.0899 577.6045 12.680747 0.0009271079
## 15 0.37661718 12 -276.2361 577.8968 12.973067 0.0008010395
## 16 0.30773340 12 -276.3047 578.0341 13.110305 0.0007479162
## 17 0.27658548 12 -276.3785 578.1816 13.257781 0.0006947507
## 18 0.37585967 12 -276.5576 578.5399 13.616081 0.0005807981
## 19 0.33599631 12 -277.5640 580.5526 15.628803 0.0002123088
## 20 -0.09731483 12 -278.2477 581.9201 16.996315 0.0001071560
## Dataset
## 1 osm_final_df_2021_2022
## 2 osm_final_df_2021_2022
## 3 osm_final_df_2021_2022
## 4 osm_final_df_2021_2022
## 5 osm_final_df_2021_2022
## 6 osm_final_df_2021_2022
## 7 osm_final_df_2021_2022
## 8 osm_final_df_2021_2022
## 9 osm_final_df_2021_2022
## 10 osm_final_df_2021_2022
## 11 osm_final_df_2021_2022
## 12 osm_final_df_2021_2022
## 13 osm_final_df_2021_2022
## 14 osm_final_df_2021_2022
## 15 osm_final_df_2021_2022
## 16 osm_final_df_2021_2022
## 17 osm_final_df_2021_2022
## 18 osm_final_df_2021_2022
## 19 osm_final_df_2021_2022
## 20 osm_final_df_2021_2022Note about re-run global models: when we re-ran the global models with dropped correlated variables our top model for fisher did not change but the weight did increase (0.266 - 0.526)
Model summary 1000m
Let’s print the summary for this model
summary(fisher_mods$`1000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(fisher, absent_fisher) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## scale(lc_grassland) + scale(lc_developed) + scale(lc_forest) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 563.5 604.9 -269.7 539.5 220
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.2993 0.5471
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.09976 0.25568 -12.124 < 2e-16 ***
## scale(harvest) -0.24280 0.10744 -2.260 0.02383 *
## scale(seismic_lines) 0.02617 0.09586 0.273 0.78482
## scale(seismic_lines_3D) -0.37457 0.16863 -2.221 0.02633 *
## scale(trails) 0.09436 0.09191 1.027 0.30457
## scale(wells) 0.04744 0.11174 0.425 0.67117
## scale(osm_industrial) -0.02086 0.13613 -0.153 0.87821
## scale(pipeline_transmission_lines) -0.59158 0.19212 -3.079 0.00208 **
## scale(lc_grassland) 0.21162 0.11357 1.863 0.06240 .
## scale(lc_developed) 0.57274 0.12387 4.624 3.77e-06 ***
## scale(lc_forest) 0.70616 0.17333 4.074 4.62e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Everything looks good
Subset models
Add later if deemed necessary.
Grey wolf
Global models
wolf_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(grey_wolf, absent_grey_wolf) ~
# HFI
scale(harvest) +
# scale(pipeline) +
# scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
# scale(transmission_lines) +
# scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# VEG covariates in numerical order
scale(lc_grassland) +
# scale(lc_coniferous) +
# scale(lc_broadleaf) +
# scale(lc_mixed) +
scale(lc_developed) +
# scale(lc_shrub) +
scale(lc_forest) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
wolf_global_model.sel <- model.sel(wolf_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_final_df_2021_2022)))
# look at model selection results
wolf_global_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 3500 meter buffer -3.241080 + 0.194223499
## 2 3750 meter buffer -3.240742 + 0.204414444
## 3 3250 meter buffer -3.238011 + 0.180711889
## 4 4000 meter buffer -3.228914 + 0.184087788
## 5 3000 meter buffer -3.237625 + 0.188638735
## 6 4250 meter buffer -3.210211 + 0.122195256
## 7 2250 meter buffer -3.270770 + 0.136419264
## 8 2750 meter buffer -3.244702 + 0.190325800
## 9 2000 meter buffer -3.264612 + 0.104390609
## 10 2500 meter buffer -3.253495 + 0.165116931
## 11 4500 meter buffer -3.187178 + -0.023640642
## 12 1750 meter buffer -3.248178 + 0.076996670
## 13 4750 meter buffer -3.190901 + 0.076094150
## 14 5000 meter buffer -3.187831 + 0.077807145
## 15 1500 meter buffer -3.223890 + 0.072341544
## 16 1250 meter buffer -3.189647 + 0.081338565
## 17 1000 meter buffer -3.172840 + 0.067841875
## 18 250 meter buffer -3.212090 + 0.024559128
## 19 500 meter buffer -3.158348 + -0.002271218
## 20 750 meter buffer -3.149762 + 0.069943373
## cond(scale(lc_developed)) cond(scale(lc_forest)) cond(scale(lc_grassland))
## 1 0.0266379039 0.130807264 0.56008161
## 2 0.0335144227 0.140248688 0.59059966
## 3 0.0108659999 0.127471908 0.49866982
## 4 -0.0353683654 0.145464449 0.58873246
## 5 0.0003671201 0.103144877 0.43691208
## 6 -0.0919821895 0.144099863 0.54808555
## 7 0.3242142495 0.197838750 0.26464102
## 8 0.0534622598 0.119950805 0.38445326
## 9 0.3375102315 0.218123998 0.21946042
## 10 0.1836977775 0.141899075 0.30712638
## 11 -0.2649012675 0.119859963 0.47802293
## 12 0.3188754516 0.206844464 0.15508903
## 13 -0.0583890028 0.153772791 0.51131542
## 14 -0.0061590542 0.155431790 0.50951315
## 15 0.2962775957 0.173769189 0.09912091
## 16 0.1545603950 0.093511055 0.08397110
## 17 0.0476211815 0.044090850 0.06164422
## 18 -0.1363311460 -0.016463872 -0.06204844
## 19 -0.0650089131 -0.110366774 -0.12392074
## 20 -0.0863735367 -0.008995222 0.04169226
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 -0.18346209 -0.163264614
## 2 -0.17143390 -0.204513608
## 3 -0.17058828 -0.111780422
## 4 -0.13712128 -0.239481764
## 5 -0.17488419 -0.073017237
## 6 -0.09837332 -0.237529692
## 7 -0.33712071 -0.016122713
## 8 -0.18851666 -0.053470219
## 9 -0.37561093 0.002686450
## 10 -0.25878440 -0.017175620
## 11 -0.04722503 -0.162486880
## 12 -0.34617300 0.007224684
## 13 -0.08593282 -0.306618307
## 14 -0.09069219 -0.359842722
## 15 -0.32426234 -0.000869049
## 16 -0.27584786 -0.006260505
## 17 -0.19445813 0.008650959
## 18 0.07144123 0.028608625
## 19 -0.06535686 0.016396072
## 20 -0.10912202 0.037890982
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.7268749 -0.024568131 0.09243789
## 2 -0.6842827 -0.011821369 0.07760084
## 3 -0.7746113 -0.012520438 0.10367713
## 4 -0.6795778 -0.028688382 0.06075201
## 5 -0.8349898 0.008779830 0.09972455
## 6 -0.7003180 -0.034948126 0.07843057
## 7 -0.8264356 0.038137186 0.19985299
## 8 -0.8662871 0.026599838 0.11656928
## 9 -0.8083138 0.019235289 0.22958289
## 10 -0.8666287 0.044026474 0.16640039
## 11 -0.8085713 -0.056138944 0.11889953
## 12 -0.7935841 -0.067953607 0.22576119
## 13 -0.6687589 -0.042685508 0.08722261
## 14 -0.6356450 -0.039324251 0.08120180
## 15 -0.7546658 -0.044327801 0.21076902
## 16 -0.7537462 -0.002609989 0.15811719
## 17 -0.7956591 0.046574433 0.10346726
## 18 -1.1088557 0.013091754 0.22047546
## 19 -0.8088531 0.067420837 0.10091086
## 20 -0.8334033 0.116771888 0.03224661
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.40673585 12 -241.0302 507.4851 0.0000000 3.026598e-01
## 2 -0.43915770 12 -241.2530 507.9307 0.4456331 2.422070e-01
## 3 -0.39186371 12 -241.8104 509.0454 1.5602873 1.387211e-01
## 4 -0.37871183 12 -241.9762 509.3770 1.8919361 1.175238e-01
## 5 -0.34473727 12 -242.9303 511.2853 3.8002410 4.526295e-02
## 6 -0.33626172 12 -243.2758 511.9763 4.4911795 3.204110e-02
## 7 -0.49617187 12 -243.4311 512.2869 4.8017985 2.743199e-02
## 8 -0.33948678 12 -243.5383 512.5013 5.0161828 2.464362e-02
## 9 -0.45732440 12 -243.6632 512.7510 5.2658950 2.175104e-02
## 10 -0.40503696 12 -243.9819 513.3885 5.9034177 1.581408e-02
## 11 -0.23365281 12 -244.1118 513.6482 6.1631581 1.388807e-02
## 12 -0.42117886 12 -244.6348 514.6942 7.2091136 8.232193e-03
## 13 -0.29135251 12 -245.2127 515.8501 8.3649668 4.618758e-03
## 14 -0.29237162 12 -245.8419 517.1085 9.6234240 2.461811e-03
## 15 -0.36824066 12 -245.9238 517.2723 9.7871929 2.268260e-03
## 16 -0.28705839 12 -247.9546 521.3339 13.8488585 2.976551e-04
## 17 -0.27582360 12 -249.3812 524.1871 16.7020003 7.147619e-05
## 18 -0.06846463 12 -249.7165 524.8576 17.3725038 5.111677e-05
## 19 -0.30235731 12 -249.8445 525.1136 17.6285078 4.497517e-05
## 20 -0.12850018 12 -251.4317 528.2880 20.8029240 9.197231e-06
## Dataset
## 1 osm_final_df_2021_2022
## 2 osm_final_df_2021_2022
## 3 osm_final_df_2021_2022
## 4 osm_final_df_2021_2022
## 5 osm_final_df_2021_2022
## 6 osm_final_df_2021_2022
## 7 osm_final_df_2021_2022
## 8 osm_final_df_2021_2022
## 9 osm_final_df_2021_2022
## 10 osm_final_df_2021_2022
## 11 osm_final_df_2021_2022
## 12 osm_final_df_2021_2022
## 13 osm_final_df_2021_2022
## 14 osm_final_df_2021_2022
## 15 osm_final_df_2021_2022
## 16 osm_final_df_2021_2022
## 17 osm_final_df_2021_2022
## 18 osm_final_df_2021_2022
## 19 osm_final_df_2021_2022
## 20 osm_final_df_2021_2022Note about re-run global models: when we re-ran the global models with dropped correlated variables our top model for wolf did change (500 - 3500), as with coyote and caribou this is more similar to top anthro modelthan it was before AND actually the same as the landscape model
Model summary 3500m
Let’s get the model summary for this model
summary(wolf_mods$`3500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(grey_wolf, absent_grey_wolf) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## scale(lc_grassland) + scale(lc_developed) + scale(lc_forest) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 506.1 547.4 -241.0 482.1 220
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.1303 0.3609
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.24108 0.19902 -16.285 < 2e-16 ***
## scale(harvest) 0.19422 0.16181 1.200 0.230006
## scale(seismic_lines) -0.02457 0.13065 -0.188 0.850840
## scale(seismic_lines_3D) -0.72687 0.29274 -2.483 0.013026 *
## scale(trails) 0.09244 0.11377 0.812 0.416513
## scale(wells) -0.40674 0.27482 -1.480 0.138866
## scale(osm_industrial) -0.18346 0.16334 -1.123 0.261349
## scale(pipeline_transmission_lines) -0.16326 0.19750 -0.827 0.408440
## scale(lc_grassland) 0.56008 0.15213 3.682 0.000232 ***
## scale(lc_developed) 0.02664 0.28028 0.095 0.924282
## scale(lc_forest) 0.13081 0.19835 0.659 0.509581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Subset models
Add later if deemed necessary.
Lynx
Global models
lynx_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(lynx, absent_lynx) ~
# HFI
scale(harvest) +
# scale(pipeline) +
# scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
# scale(transmission_lines) +
# scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# VEG covariates in numerical order
scale(lc_grassland) +
# scale(lc_coniferous) +
# scale(lc_broadleaf) +
# scale(lc_mixed) +
scale(lc_developed) +
# scale(lc_shrub) +
scale(lc_forest) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
# run model selection and save the results as a tibble for graphing use later
lynx_global_model.sel <- model.sel(lynx_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_final_df_2021_2022)))
# look at model selection results
lynx_global_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 250 meter buffer -1.898428 + -0.302883298
## 2 500 meter buffer -1.884363 + -0.217519901
## 3 3250 meter buffer -1.887696 + 0.169175844
## 4 3000 meter buffer -1.887112 + 0.159699656
## 5 1000 meter buffer -1.872601 + -0.106823652
## 6 3500 meter buffer -1.886305 + 0.172124772
## 7 1250 meter buffer -1.873243 + -0.089823808
## 8 750 meter buffer -1.874501 + -0.149993834
## 9 3750 meter buffer -1.885218 + 0.175625754
## 10 2750 meter buffer -1.883255 + 0.138658729
## 11 1500 meter buffer -1.872891 + -0.059072154
## 12 4250 meter buffer -1.881576 + 0.180076272
## 13 4000 meter buffer -1.882743 + 0.177327069
## 14 5000 meter buffer -1.874674 + 0.129414209
## 15 4750 meter buffer -1.875063 + 0.145949568
## 16 4500 meter buffer -1.877739 + 0.170751654
## 17 2000 meter buffer -1.872898 + 0.040975998
## 18 2500 meter buffer -1.877010 + 0.105105374
## 19 1750 meter buffer -1.870128 + -0.006825369
## 20 2250 meter buffer -1.873867 + 0.079934227
## cond(scale(lc_developed)) cond(scale(lc_forest)) cond(scale(lc_grassland))
## 1 -0.13488291 -0.018564129 0.01084969
## 2 -0.09147433 0.046896290 0.16345357
## 3 0.14348731 0.012155250 0.16577245
## 4 0.16203947 0.020307082 0.17251629
## 5 -0.01345159 0.077618131 0.13004422
## 6 0.13143181 -0.000369871 0.13617464
## 7 0.07801732 0.087879200 0.14980992
## 8 -0.04746628 0.075406071 0.12483804
## 9 0.12877075 -0.006577550 0.09997960
## 10 0.16534886 0.021998047 0.16321158
## 11 0.14371928 0.090204396 0.14671108
## 12 0.09101602 0.002492953 0.06487366
## 13 0.10850337 -0.003900798 0.07577853
## 14 0.04349666 0.029509102 -0.01200343
## 15 0.05866018 0.019609829 -0.01060870
## 16 0.06515203 0.012416363 0.02204842
## 17 0.18325868 0.080492043 0.10730576
## 18 0.17998278 0.038024831 0.11925861
## 19 0.15908625 0.083789380 0.10560206
## 20 0.18793882 0.060207261 0.09285951
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 0.100523215 0.003730106
## 2 0.122347187 -0.090041949
## 3 0.008736217 -0.103725280
## 4 0.002711300 -0.105178103
## 5 0.081673167 -0.035018474
## 6 0.001590802 -0.087949023
## 7 0.043882423 -0.051076813
## 8 0.107609728 -0.045020960
## 9 0.000424834 -0.079782167
## 10 0.003943819 -0.092814796
## 11 0.019547119 -0.051093766
## 12 0.025073675 -0.076595967
## 13 0.010306852 -0.081497476
## 14 0.078185372 -0.074204591
## 15 0.062992452 -0.064440637
## 16 0.047234375 -0.076090157
## 17 0.014519556 -0.046310037
## 18 0.007504534 -0.082022647
## 19 0.018253166 -0.038411426
## 20 0.014027316 -0.068024487
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 0.07553842 -0.172539784 0.04983365
## 2 0.07648397 -0.090876141 -0.01788460
## 3 0.11650660 -0.137848940 0.09311238
## 4 0.12049832 -0.106940450 0.09253440
## 5 0.07623316 -0.001316084 0.10100901
## 6 0.10397270 -0.158838354 0.09991898
## 7 0.09034415 0.061223149 0.11412406
## 8 0.07205674 -0.038617170 0.03278183
## 9 0.09587350 -0.175627853 0.09758097
## 10 0.11958993 -0.083159007 0.08370259
## 11 0.10155633 0.067106873 0.12622060
## 12 0.09533020 -0.177536746 0.09542228
## 13 0.09408521 -0.175549224 0.09327548
## 14 0.10169883 -0.167344366 0.09053452
## 15 0.09295081 -0.163797883 0.09616824
## 16 0.09271259 -0.169661588 0.09431668
## 17 0.10571156 0.026895854 0.11284790
## 18 0.11113085 -0.062454010 0.08584787
## 19 0.10152780 0.043044697 0.11681140
## 20 0.10682084 -0.035860471 0.09529744
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.2172774 12 -436.0433 897.5113 0.000000 9.818848e-01
## 2 -0.2136561 12 -440.2247 905.8741 8.362756 1.500068e-02
## 3 -0.2843353 12 -443.6425 912.7096 15.198320 4.917986e-04
## 4 -0.3064524 12 -443.6498 912.7243 15.212972 4.882090e-04
## 5 -0.2315831 12 -444.1067 913.6381 16.126814 3.091486e-04
## 6 -0.2578488 12 -444.1116 913.6479 16.136540 3.076489e-04
## 7 -0.2692156 12 -444.2216 913.8678 16.356502 2.756076e-04
## 8 -0.1956949 12 -444.4866 914.3978 16.886454 2.114529e-04
## 9 -0.2372007 12 -444.5271 914.4788 16.967488 2.030567e-04
## 10 -0.3160955 12 -444.6874 914.7995 17.288225 1.729697e-04
## 11 -0.2865068 12 -444.8712 915.1671 17.655828 1.439283e-04
## 12 -0.1956705 12 -445.1794 915.7835 18.272220 1.057544e-04
## 13 -0.2017007 12 -445.3383 916.1012 18.589894 9.022281e-05
## 14 -0.1283398 12 -445.7359 916.8965 19.385148 6.062183e-05
## 15 -0.1401878 12 -445.7923 917.0092 19.497911 5.729847e-05
## 16 -0.1480991 12 -445.8236 917.0719 19.560561 5.553141e-05
## 17 -0.3164331 12 -445.9946 917.4138 19.902508 4.680430e-05
## 18 -0.3023679 12 -446.2781 917.9808 20.469475 3.525092e-05
## 19 -0.2939078 12 -446.3129 918.0504 20.539115 3.404460e-05
## 20 -0.2986471 12 -446.6142 918.6530 21.141701 2.518828e-05
## Dataset
## 1 osm_final_df_2021_2022
## 2 osm_final_df_2021_2022
## 3 osm_final_df_2021_2022
## 4 osm_final_df_2021_2022
## 5 osm_final_df_2021_2022
## 6 osm_final_df_2021_2022
## 7 osm_final_df_2021_2022
## 8 osm_final_df_2021_2022
## 9 osm_final_df_2021_2022
## 10 osm_final_df_2021_2022
## 11 osm_final_df_2021_2022
## 12 osm_final_df_2021_2022
## 13 osm_final_df_2021_2022
## 14 osm_final_df_2021_2022
## 15 osm_final_df_2021_2022
## 16 osm_final_df_2021_2022
## 17 osm_final_df_2021_2022
## 18 osm_final_df_2021_2022
## 19 osm_final_df_2021_2022
## 20 osm_final_df_2021_2022Note about re-run global models: when we re-ran the global models with dropped correlated variables our top model for lynx did change (1250 - 250), as with others this is more similar to top anthro model (acutally the same as anthro model).
Model summary 250m
Let’s get the model summary
summary(lynx_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(lynx, absent_lynx) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## scale(lc_grassland) + scale(lc_developed) + scale(lc_forest) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 896.1 937.4 -436.0 872.1 220
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.1573 0.3967
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.89843 0.17291 -10.980 < 2e-16 ***
## scale(harvest) -0.30288 0.09009 -3.362 0.000774 ***
## scale(seismic_lines) -0.17254 0.07219 -2.390 0.016840 *
## scale(seismic_lines_3D) 0.07554 0.05476 1.379 0.167755
## scale(trails) 0.04983 0.05307 0.939 0.347748
## scale(wells) -0.21728 0.07372 -2.947 0.003204 **
## scale(osm_industrial) 0.10052 0.05392 1.864 0.062295 .
## scale(pipeline_transmission_lines) 0.00373 0.06477 0.058 0.954079
## scale(lc_grassland) 0.01085 0.07148 0.152 0.879349
## scale(lc_developed) -0.13488 0.07953 -1.696 0.089878 .
## scale(lc_forest) -0.01856 0.07703 -0.241 0.809563
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Everything looks good so far
Subset models
Add later if deemed necessary.
Moose
Global model
moose_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(moose, absent_moose) ~
# HFI
scale(harvest) +
# scale(pipeline) +
# scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
# scale(transmission_lines) +
# scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# VEG covariates in numerical order
scale(lc_grassland) +
# scale(lc_coniferous) +
# scale(lc_broadleaf) +
# scale(lc_mixed) +
scale(lc_developed) +
# scale(lc_shrub) +
scale(lc_forest) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
moose_global_model.sel <- model.sel(moose_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_final_df_2021_2022)))
# look at model selection results
moose_global_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 250 meter buffer -1.849716 + -0.010623977
## 2 500 meter buffer -1.845364 + 0.007943112
## 3 750 meter buffer -1.842638 + 0.022287853
## 4 1000 meter buffer -1.839316 + 0.002888910
## 5 1250 meter buffer -1.831966 + 0.022185769
## 6 1500 meter buffer -1.832178 + 0.017689199
## 7 1750 meter buffer -1.829419 + 0.005967840
## 8 2500 meter buffer -1.827361 + -0.020626181
## 9 2750 meter buffer -1.827564 + -0.020803203
## 10 3000 meter buffer -1.827156 + -0.024106844
## 11 3250 meter buffer -1.824838 + -0.033285815
## 12 2000 meter buffer -1.826526 + -0.010088945
## 13 2250 meter buffer -1.826150 + -0.008670335
## 14 3500 meter buffer -1.822505 + -0.046408038
## 15 3750 meter buffer -1.820495 + -0.058906593
## 16 4000 meter buffer -1.817861 + -0.067566986
## 17 5000 meter buffer -1.812549 + -0.107067715
## 18 4250 meter buffer -1.814628 + -0.080320286
## 19 4750 meter buffer -1.812394 + -0.100470793
## 20 4500 meter buffer -1.812833 + -0.092628237
## cond(scale(lc_developed)) cond(scale(lc_forest)) cond(scale(lc_grassland))
## 1 -0.400092436 -0.28907134 -0.113092601
## 2 -0.247704490 -0.20865494 -0.028001319
## 3 -0.171449659 -0.12682403 0.012860985
## 4 -0.171733651 -0.15426764 -0.005877747
## 5 -0.153240510 -0.16737297 0.026716591
## 6 -0.078239025 -0.16386674 0.026176801
## 7 -0.067752008 -0.15424500 0.037204866
## 8 -0.011378206 -0.13542131 -0.005974313
## 9 0.003767231 -0.13334998 0.007117298
## 10 0.032777308 -0.12002779 -0.015150884
## 11 0.047794505 -0.10987427 -0.029267529
## 12 -0.018673900 -0.13426643 0.015523748
## 13 -0.025813352 -0.14134645 -0.004633224
## 14 0.047442646 -0.10794539 -0.053202040
## 15 0.046925176 -0.10971632 -0.085059615
## 16 0.043235063 -0.09741685 -0.113922120
## 17 0.047949825 -0.06937349 -0.227470774
## 18 0.041375005 -0.08017510 -0.123150351
## 19 0.062711363 -0.06349064 -0.160060453
## 20 0.043464043 -0.07208039 -0.128062905
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 -0.1807698 0.04241050
## 2 -0.2957918 0.07822214
## 3 -0.3382014 0.10344898
## 4 -0.3006834 0.10277282
## 5 -0.2423918 0.10130518
## 6 -0.2504926 0.10059183
## 7 -0.1992674 0.11300329
## 8 -0.2374117 0.20235694
## 9 -0.2614937 0.21296301
## 10 -0.2740314 0.21857662
## 11 -0.2843232 0.21815978
## 12 -0.1878836 0.13048425
## 13 -0.2027139 0.16861613
## 14 -0.2894988 0.22713706
## 15 -0.2964070 0.23506486
## 16 -0.2822088 0.22773893
## 17 -0.1979284 0.25777804
## 18 -0.2577785 0.20753102
## 19 -0.2124468 0.20998896
## 20 -0.2348222 0.19885008
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 0.1464444607 0.105069523 0.08050523
## 2 0.1163658557 0.073922718 0.05705261
## 3 0.0916389522 0.003155747 0.04281248
## 4 0.0680339795 -0.065833553 0.06115504
## 5 0.0580559161 -0.046035692 0.07610496
## 6 0.0567591650 -0.060175421 0.08446437
## 7 0.0397587118 -0.068108737 0.06578645
## 8 -0.0171895911 -0.014274674 0.08757601
## 9 -0.0211132958 -0.014686468 0.08737380
## 10 -0.0239053783 -0.032256841 0.09076933
## 11 -0.0276048704 -0.032759875 0.09238102
## 12 0.0213802946 -0.037162312 0.06642959
## 13 -0.0006894857 -0.022439828 0.07148073
## 14 -0.0353599264 -0.031967269 0.09331236
## 15 -0.0442201256 -0.028397004 0.09740065
## 16 -0.0519282356 -0.028003814 0.10050589
## 17 -0.0745985386 0.026541061 0.10798249
## 18 -0.0498499039 -0.014473141 0.10404817
## 19 -0.0522331227 0.020226622 0.10470023
## 20 -0.0486323061 0.003291123 0.10496804
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.03034155 12 -427.0835 879.5916 0.000000 9.408427e-01
## 2 -0.05981923 12 -430.5929 886.6104 7.018784 2.814541e-02
## 3 -0.12909875 12 -431.1488 887.7223 8.130656 1.614237e-02
## 4 -0.16432321 12 -431.3512 888.1271 8.535477 1.318444e-02
## 5 -0.14408258 12 -434.2785 893.9816 14.389964 7.059534e-04
## 6 -0.19605163 12 -434.9712 895.3671 15.775486 3.531130e-04
## 7 -0.25988910 12 -435.7040 896.8326 17.241033 1.696970e-04
## 8 -0.27095540 12 -436.4442 898.3131 18.721480 8.094671e-05
## 9 -0.25671814 12 -436.6093 898.6433 19.051689 6.862703e-05
## 10 -0.26306339 12 -436.6754 898.7755 19.183847 6.423882e-05
## 11 -0.24348919 12 -437.0982 899.6211 20.029473 4.208936e-05
## 12 -0.29839896 12 -437.1559 899.7365 20.144839 3.973021e-05
## 13 -0.27641726 12 -437.2031 899.8309 20.239324 3.789690e-05
## 14 -0.21965224 12 -437.2852 899.9951 20.403517 3.490998e-05
## 15 -0.17824883 12 -437.4496 900.3240 20.732340 2.961737e-05
## 16 -0.15067915 12 -437.7959 901.0164 21.424760 2.095027e-05
## 17 -0.14820778 12 -438.0026 901.4298 21.838219 1.703758e-05
## 18 -0.13819369 12 -438.6585 902.7417 23.150119 8.841632e-06
## 19 -0.17151672 12 -438.9892 903.4031 23.811493 6.352095e-06
## 20 -0.14419802 12 -439.2096 903.8438 24.252202 5.095869e-06
## Dataset
## 1 osm_final_df_2021_2022
## 2 osm_final_df_2021_2022
## 3 osm_final_df_2021_2022
## 4 osm_final_df_2021_2022
## 5 osm_final_df_2021_2022
## 6 osm_final_df_2021_2022
## 7 osm_final_df_2021_2022
## 8 osm_final_df_2021_2022
## 9 osm_final_df_2021_2022
## 10 osm_final_df_2021_2022
## 11 osm_final_df_2021_2022
## 12 osm_final_df_2021_2022
## 13 osm_final_df_2021_2022
## 14 osm_final_df_2021_2022
## 15 osm_final_df_2021_2022
## 16 osm_final_df_2021_2022
## 17 osm_final_df_2021_2022
## 18 osm_final_df_2021_2022
## 19 osm_final_df_2021_2022
## 20 osm_final_df_2021_2022Note about re-run global models: when we re-ran the global models with dropped correlated variables our top model for moose did not change but the weight did increase (0.504 - 0.941). This top model is the same as the landscape model.
Model summary 250m
Let’s get the model summary
summary(moose_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(moose, absent_moose) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## scale(lc_grassland) + scale(lc_developed) + scale(lc_forest) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 878.2 919.5 -427.1 854.2 220
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.3014 0.549
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.84972 0.23284 -7.944 1.95e-15 ***
## scale(harvest) -0.01062 0.06092 -0.174 0.861546
## scale(seismic_lines) 0.10507 0.06305 1.666 0.095643 .
## scale(seismic_lines_3D) 0.14644 0.05514 2.656 0.007909 **
## scale(trails) 0.08051 0.05258 1.531 0.125766
## scale(wells) -0.03034 0.06728 -0.451 0.652026
## scale(osm_industrial) -0.18077 0.07146 -2.530 0.011420 *
## scale(pipeline_transmission_lines) 0.04241 0.06953 0.610 0.541879
## scale(lc_grassland) -0.11309 0.06932 -1.631 0.102817
## scale(lc_developed) -0.40009 0.08750 -4.572 4.82e-06 ***
## scale(lc_forest) -0.28907 0.08148 -3.548 0.000388 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Looks good for now
Subset models
Add later if deemed necessary
Red fox
Global models
fox_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(red_fox, absent_red_fox) ~
# HFI
scale(harvest) +
# scale(pipeline) +
# scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
# scale(transmission_lines) +
# scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# VEG covariates in numerical order
scale(lc_grassland) +
# scale(lc_coniferous) +
# scale(lc_broadleaf) +
# scale(lc_mixed) +
scale(lc_developed) +
# scale(lc_shrub) +
scale(lc_forest) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
fox_global_model.sel <- model.sel(fox_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_final_df_2021_2022)))
# look at model selection results
fox_global_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 4750 meter buffer -4.710660 + 0.1719449706
## 2 5000 meter buffer -4.438342 + 0.1962039685
## 3 4500 meter buffer -4.704824 + 0.1479952639
## 4 1500 meter buffer -4.533097 + -0.2459263767
## 5 1750 meter buffer -4.593444 + -0.1464567284
## 6 4250 meter buffer -4.582045 + 0.1111945290
## 7 4000 meter buffer -4.307353 + 0.0898085271
## 8 1250 meter buffer -4.413307 + -0.2390297495
## 9 3750 meter buffer -4.259312 + 0.0325518386
## 10 2000 meter buffer -4.463057 + -0.0959096352
## 11 2250 meter buffer -4.206357 + -0.0516943115
## 12 2500 meter buffer -4.185386 + -0.0026727758
## 13 1000 meter buffer -4.423257 + -0.2049562281
## 14 2750 meter buffer -4.165725 + 0.0474697062
## 15 500 meter buffer -4.331585 + -0.1191625937
## 16 3500 meter buffer -4.159307 + 0.0000326624
## 17 3000 meter buffer -4.150423 + 0.0533881937
## 18 3250 meter buffer -4.148094 + 0.0346435735
## 19 750 meter buffer -4.350467 + -0.2256966123
## 20 250 meter buffer -4.314438 + -0.0755865998
## cond(scale(lc_developed)) cond(scale(lc_forest)) cond(scale(lc_grassland))
## 1 0.3922000 -0.79371553 0.4438821
## 2 0.6080074 -0.62825324 0.4975331
## 3 0.3742119 -0.79675787 0.3943830
## 4 0.4050962 -0.12634316 0.5745372
## 5 0.2375217 -0.16775374 0.5892874
## 6 0.2584423 -0.76448355 0.2998805
## 7 0.5899049 -0.45664133 0.4669497
## 8 0.4211023 -0.10873136 0.5176597
## 9 0.6150069 -0.31440316 0.5523548
## 10 0.3378334 -0.10968856 0.6123616
## 11 0.7396066 -0.01560293 0.7021326
## 12 0.7367246 -0.06084646 0.6474266
## 13 0.5975677 -0.04113826 0.5440339
## 14 0.6695471 -0.11748636 0.5551106
## 15 0.6214101 0.36552881 0.3195532
## 16 0.6637446 -0.20005475 0.5829544
## 17 0.6574876 -0.13367496 0.5417045
## 18 0.5885693 -0.13511957 0.5874901
## 19 0.4363296 0.11421495 0.5122062
## 20 0.5478319 0.14059910 0.2997906
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 0.211508231 0.85133918
## 2 0.024189383 0.36202796
## 3 0.204850332 0.83388673
## 4 0.167290751 -0.26360179
## 5 0.303308901 -0.09302959
## 6 0.039526359 0.75986990
## 7 -0.497790096 0.20961822
## 8 0.165419273 -0.58176206
## 9 -0.567369169 0.10938966
## 10 0.234716202 -0.22122562
## 11 -0.006097739 -0.47668309
## 12 -0.019592999 -0.39525624
## 13 0.130117162 -0.92467132
## 14 -0.053907992 -0.29316939
## 15 0.363842690 -0.34869971
## 16 -0.253684805 -0.05723367
## 17 -0.108209737 -0.18213745
## 18 -0.139049284 -0.17195934
## 19 0.221464242 -0.37059674
## 20 0.192562170 -0.40468512
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.64074029 -0.566613856 -0.8160982
## 2 -1.29237310 -0.236530689 -0.8335380
## 3 -0.59594635 -0.517839825 -0.8812319
## 4 -0.38786539 -0.121790744 -0.7753648
## 5 -0.47177828 -0.191262989 -0.8771643
## 6 -0.72239778 -0.414262359 -0.7283896
## 7 -1.01320298 -0.054081886 -0.6360047
## 8 -0.39443681 -0.092135933 -0.7080355
## 9 -0.89167770 -0.001070778 -0.4701606
## 10 -0.50567915 -0.141011833 -0.7710160
## 11 -0.65924372 0.162949023 -0.6500051
## 12 -0.62464367 0.136696676 -0.6584918
## 13 -0.27191167 -0.067027615 -0.6733136
## 14 -0.65566887 0.110873539 -0.6456739
## 15 -0.25272386 -0.173350818 -0.6186467
## 16 -0.69819491 0.040427738 -0.4202069
## 17 -0.64932567 0.102741540 -0.5829771
## 18 -0.70826784 0.088240847 -0.4496556
## 19 -0.16543996 -0.052969716 -0.6205387
## 20 0.04142461 -0.087317739 -0.8328836
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.8512377 12 -163.4806 352.3859 0.00000000 3.667349e-01
## 2 -0.4373352 12 -163.4941 352.4129 0.02703244 3.618113e-01
## 3 -0.8090613 12 -164.1207 353.6660 1.28007890 1.933689e-01
## 4 -0.5035217 12 -165.9899 357.4045 5.01857219 2.982518e-02
## 5 -0.4425342 12 -166.5185 358.4616 6.07571924 1.758031e-02
## 6 -0.4445907 12 -166.7989 359.0224 6.63645281 1.328204e-02
## 7 -0.1573813 12 -167.5841 360.5929 8.20696478 6.056649e-03
## 8 -0.1627423 12 -168.5672 362.5591 10.17320378 2.266048e-03
## 9 -0.1406339 12 -168.6182 362.6611 10.27517847 2.153404e-03
## 10 -0.2947945 12 -168.7688 362.9622 10.57633247 1.852383e-03
## 11 -0.1999088 12 -168.8712 363.1670 10.78114201 1.672080e-03
## 12 -0.2297243 12 -169.1973 363.8192 11.43326206 1.206843e-03
## 13 -0.1583967 12 -169.2443 363.9133 11.52736861 1.151373e-03
## 14 -0.1508334 12 -170.5275 366.4796 14.09369785 3.191131e-04
## 15 0.2243238 12 -170.8717 367.1680 14.78206875 2.261865e-04
## 16 -0.2561072 12 -170.9352 367.2951 14.90922512 2.122536e-04
## 17 -0.1699877 12 -171.4053 368.2353 15.84943374 1.326452e-04
## 18 -0.1065284 12 -171.8659 369.1564 16.77051761 8.369135e-05
## 19 -0.1641260 12 -172.4404 370.3054 17.91950105 4.711747e-05
## 20 0.1147348 12 -173.4257 372.2761 19.89023178 1.758908e-05
## Dataset
## 1 osm_final_df_2021_2022
## 2 osm_final_df_2021_2022
## 3 osm_final_df_2021_2022
## 4 osm_final_df_2021_2022
## 5 osm_final_df_2021_2022
## 6 osm_final_df_2021_2022
## 7 osm_final_df_2021_2022
## 8 osm_final_df_2021_2022
## 9 osm_final_df_2021_2022
## 10 osm_final_df_2021_2022
## 11 osm_final_df_2021_2022
## 12 osm_final_df_2021_2022
## 13 osm_final_df_2021_2022
## 14 osm_final_df_2021_2022
## 15 osm_final_df_2021_2022
## 16 osm_final_df_2021_2022
## 17 osm_final_df_2021_2022
## 18 osm_final_df_2021_2022
## 19 osm_final_df_2021_2022
## 20 osm_final_df_2021_2022Note about re-run global models: when we re-ran the global models with dropped correlated variables our top model for fox did not change but the weight dropped (0.855 - 0.367). This top model is the same as the landscape model.
Model summary 4750m
Let’s get the model summary
summary(fox_mods$`4750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(red_fox, absent_red_fox) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## scale(lc_grassland) + scale(lc_developed) + scale(lc_forest) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 351.0 392.3 -163.5 327.0 220
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 1.334 1.155
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.7107 0.6049 -7.788 6.83e-15 ***
## scale(harvest) 0.1719 0.2779 0.619 0.53603
## scale(seismic_lines) -0.5666 0.2969 -1.909 0.05632 .
## scale(seismic_lines_3D) -0.6407 0.6589 -0.972 0.33086
## scale(trails) -0.8161 0.4000 -2.040 0.04131 *
## scale(wells) -0.8512 0.4063 -2.095 0.03617 *
## scale(osm_industrial) 0.2115 0.3085 0.686 0.49298
## scale(pipeline_transmission_lines) 0.8513 0.4980 1.710 0.08736 .
## scale(lc_grassland) 0.4439 0.2471 1.796 0.07242 .
## scale(lc_developed) 0.3922 0.3527 1.112 0.26617
## scale(lc_forest) -0.7937 0.2942 -2.698 0.00697 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Looks good so far
Subset Models
Add later if deemed necessary.
White-tailed deer
Global models
deer_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
# have to include the `` around the white-tailed_deer or R won't recognize it as a variable because of the -
glmmTMB::glmmTMB(cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~
# HFI
scale(harvest) +
# scale(pipeline) +
# scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
# scale(transmission_lines) +
# scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# VEG covariates in numerical order
scale(lc_grassland) +
# scale(lc_coniferous) +
# scale(lc_broadleaf) +
# scale(lc_mixed) +
scale(lc_developed) +
# scale(lc_shrub) +
scale(lc_forest) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
deer_global_model.sel <- model.sel(deer_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_final_df_2021_2022)))
# look at model selection results
deer_global_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 1500 meter buffer -0.2118018 + -0.08697915
## 2 4000 meter buffer -0.2228334 + -0.20497662
## 3 4500 meter buffer -0.2264998 + -0.23139410
## 4 4250 meter buffer -0.2244901 + -0.22030347
## 5 3750 meter buffer -0.2191663 + -0.17859059
## 6 4750 meter buffer -0.2282169 + -0.25375531
## 7 3500 meter buffer -0.2173055 + -0.15909038
## 8 5000 meter buffer -0.2304881 + -0.27597320
## 9 1250 meter buffer -0.2057161 + -0.08675304
## 10 1750 meter buffer -0.2067686 + -0.09977419
## 11 3250 meter buffer -0.2116719 + -0.13329466
## 12 3000 meter buffer -0.2055097 + -0.11846276
## 13 2000 meter buffer -0.2057676 + -0.10146795
## 14 2250 meter buffer -0.2033035 + -0.11370795
## 15 2750 meter buffer -0.2032782 + -0.10732094
## 16 2500 meter buffer -0.2043346 + -0.10765711
## 17 1000 meter buffer -0.1895958 + -0.11220105
## 18 750 meter buffer -0.1780650 + -0.07972438
## 19 500 meter buffer -0.1829497 + -0.01119863
## 20 250 meter buffer -0.1844108 + 0.04014501
## cond(scale(lc_developed)) cond(scale(lc_forest)) cond(scale(lc_grassland))
## 1 -0.183436547 -0.06969916 0.14372747
## 2 -0.087805884 -0.08854160 0.30653918
## 3 -0.031435450 -0.09763181 0.35504370
## 4 -0.066398213 -0.09771188 0.32574618
## 5 -0.110402993 -0.09364101 0.27871118
## 6 0.022210383 -0.08592438 0.35768242
## 7 -0.117084906 -0.09228863 0.26598138
## 8 0.043026444 -0.08800771 0.35136995
## 9 -0.126352063 -0.05513991 0.13263971
## 10 -0.172632016 -0.07167220 0.14085256
## 11 -0.129052360 -0.09846753 0.25219343
## 12 -0.120867001 -0.10008698 0.22913746
## 13 -0.151532753 -0.07911291 0.15437899
## 14 -0.137393389 -0.08353000 0.16053268
## 15 -0.120869553 -0.09510206 0.20523539
## 16 -0.123086455 -0.09080592 0.18918194
## 17 -0.016106577 -0.01412560 0.08257206
## 18 0.033503242 -0.01587439 0.05410520
## 19 0.019784091 -0.05613558 0.05070313
## 20 -0.004756961 -0.11237215 0.02677214
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 0.3569561 0.046930837
## 2 0.2042716 -0.086433912
## 3 0.1960369 -0.148546289
## 4 0.1993662 -0.113231511
## 5 0.2060455 -0.039671964
## 6 0.1891270 -0.203305588
## 7 0.2096988 -0.034266880
## 8 0.1822756 -0.253638260
## 9 0.3665603 0.036414972
## 10 0.3224017 0.059218440
## 11 0.2181027 -0.011204600
## 12 0.2272504 -0.001410895
## 13 0.2878043 0.039226254
## 14 0.2665277 0.020149450
## 15 0.2355525 0.003639011
## 16 0.2470561 0.008483685
## 17 0.3388702 0.052858673
## 18 0.3299672 0.098927530
## 19 0.3135973 0.116682599
## 20 0.2171382 0.093551391
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.4547173 -0.08996001 0.16851613
## 2 -0.3458370 -0.04041973 0.22183271
## 3 -0.2999718 -0.04713276 0.20995443
## 4 -0.3221222 -0.05741096 0.21309017
## 5 -0.3671571 -0.03409730 0.22323381
## 6 -0.2724234 -0.02822194 0.20937160
## 7 -0.3659442 -0.03980814 0.21630321
## 8 -0.2488776 -0.02063040 0.21272948
## 9 -0.4348374 -0.07623988 0.15513595
## 10 -0.4230096 -0.10163402 0.16782175
## 11 -0.3567852 -0.02783274 0.20691023
## 12 -0.3422108 -0.01609253 0.18994593
## 13 -0.4014505 -0.09492539 0.16499698
## 14 -0.3672114 -0.05274901 0.17912160
## 15 -0.3422297 -0.02910964 0.18797904
## 16 -0.3449757 -0.04236590 0.18781294
## 17 -0.3421214 -0.06818239 0.14888925
## 18 -0.2814914 -0.01456565 0.08956161
## 19 -0.2644586 0.03644965 0.10641335
## 20 -0.2163116 -0.02892613 0.19492800
## cond(scale(wells)) df logLik AICc delta weight
## 1 0.35761341 12 -559.5356 1144.496 0.0000000 2.336945e-01
## 2 0.35280602 12 -559.6139 1144.652 0.1566346 2.160906e-01
## 3 0.31476467 12 -559.7405 1144.906 0.4098307 1.903947e-01
## 4 0.33546987 12 -559.8449 1145.114 0.6186809 1.715156e-01
## 5 0.36811564 12 -560.4516 1146.328 1.8320648 9.350197e-02
## 6 0.30114312 12 -561.6082 1148.641 4.1452665 2.941138e-02
## 7 0.37020871 12 -562.0942 1149.613 5.1172155 1.809086e-02
## 8 0.31845188 12 -562.3844 1150.194 5.6977115 1.353338e-02
## 9 0.31896401 12 -562.4469 1150.318 5.8226493 1.271383e-02
## 10 0.34862826 12 -562.5917 1150.608 6.1122272 1.100007e-02
## 11 0.37930253 12 -562.9868 1151.398 6.9024579 7.409670e-03
## 12 0.38040277 12 -565.0241 1155.473 10.9770750 9.660665e-04
## 13 0.35382030 12 -565.3652 1156.155 11.6592803 6.868597e-04
## 14 0.37886200 12 -565.9253 1157.275 12.7795451 3.922885e-04
## 15 0.38338991 12 -566.0391 1157.503 13.0071132 3.500981e-04
## 16 0.36992273 12 -566.3869 1158.198 13.7026388 2.472626e-04
## 17 0.27620629 12 -572.1280 1169.681 25.1847907 7.940370e-07
## 18 0.25604451 12 -583.5892 1192.603 48.1072488 8.361655e-12
## 19 0.24464000 12 -588.8826 1203.190 58.6940411 4.201453e-14
## 20 0.06152785 12 -595.6261 1216.677 72.1809510 4.951699e-17
## Dataset
## 1 osm_final_df_2021_2022
## 2 osm_final_df_2021_2022
## 3 osm_final_df_2021_2022
## 4 osm_final_df_2021_2022
## 5 osm_final_df_2021_2022
## 6 osm_final_df_2021_2022
## 7 osm_final_df_2021_2022
## 8 osm_final_df_2021_2022
## 9 osm_final_df_2021_2022
## 10 osm_final_df_2021_2022
## 11 osm_final_df_2021_2022
## 12 osm_final_df_2021_2022
## 13 osm_final_df_2021_2022
## 14 osm_final_df_2021_2022
## 15 osm_final_df_2021_2022
## 16 osm_final_df_2021_2022
## 17 osm_final_df_2021_2022
## 18 osm_final_df_2021_2022
## 19 osm_final_df_2021_2022
## 20 osm_final_df_2021_2022Note about re-run global models: when we re-ran the global models with dropped correlated variables our top model for deer did not change but the weight dropped (0.974 - 0.234). This top model is the same as the landscape model.
Model summary 1500m
Let’s get the model summary
summary(deer_mods$`1500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~ scale(harvest) +
## scale(seismic_lines) + scale(seismic_lines_3D) + scale(trails) +
## scale(wells) + scale(osm_industrial) + scale(pipeline_transmission_lines) +
## scale(lc_grassland) + scale(lc_developed) + scale(lc_forest) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 1143.1 1184.4 -559.5 1119.1 220
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 1.974 1.405
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.21180 0.57592 -0.368 0.71305
## scale(harvest) -0.08698 0.06553 -1.327 0.18440
## scale(seismic_lines) -0.08996 0.06637 -1.355 0.17530
## scale(seismic_lines_3D) -0.45472 0.09650 -4.712 2.45e-06 ***
## scale(trails) 0.16852 0.05189 3.247 0.00117 **
## scale(wells) 0.35761 0.07963 4.491 7.10e-06 ***
## scale(osm_industrial) 0.35696 0.05902 6.048 1.47e-09 ***
## scale(pipeline_transmission_lines) 0.04693 0.08430 0.557 0.57771
## scale(lc_grassland) 0.14373 0.06313 2.277 0.02281 *
## scale(lc_developed) -0.18344 0.08172 -2.245 0.02479 *
## scale(lc_forest) -0.06970 0.05879 -1.186 0.23578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Looks okay for now
Subset models
Add later if deemed necessary.
Top buffers
For convenience here is a list of the top buffer widths for each species
Initial run
* Black bear - 250m
* Caribou - 3000m
* Coyote - 2250m
* Fisher - 1000m
* Grey wolf - 500m
* Lynx - 1250m
* Moose - 250m
* Red fox - 4750
* White-tailed deer - 1500m
Re-run (without correlated variables)
* Black bear - 250m
* Caribou - 1000m
* Coyote - 5000m
* Fisher - 1000m
* Grey wolf - 3500m
* Lynx - 250m
* Moose - 250m
* Red fox - 4750m
* White-tailed deer - 1500m
I also want to create a data frame with the model.sel outputs from each species so I can plot these results later
# provide list of model.sel data frames from global analysis
global_model.sel_data <- list(
bear = black_bear_global_model.sel,
caribou = caribou_global_model.sel,
coyote = coyote_global_model.sel,
fisher = fisher_global_model.sel,
wolf = wolf_global_model.sel,
lynx = lynx_global_model.sel,
moose = moose_global_model.sel,
fox = fox_global_model.sel,
deer = deer_global_model.sel
) %>%
# use purrr to combine data and extract species names to use as a column
map_dfr(~.x %>%
mutate(species = deparse(substitute(.x))),
.id = "species")And save this for use later
write_csv(global_model.sel_data,
'data/processed/OSM_glm_global_model_sel_data.csv')Analysis Prep
Summary
While the global models are interesting and yield relevant results there is an issue of addressing autocorrelation with such large models that we don’t have the capacity to address at each spatial scale while still keeping the models the same so the only thing being compared is the buffer size.
It’s also relevant to wonder if these results would be different depending on if we look at exclusively anthropogenic or landscape features in a model and by reducing the variables in each model we can address this issue of autocorrelation much easier. So the next analysis will repeat what we’ve done above for each species but instead of using one global model for everything we will divide the data into disturbance and landscape covariates and run two separate models per species and see if the results differ.
Data prep
First let’s subset the data into the two new groups we will use for this analysis so we can check for auto correlations within the data sets
Anthropogenic
First let’s generate the developed data set which includes only the covariates for the human factors indices
osm_anthro_df_2021_2022 <- osm_final_df_2021_2022 %>%
# use purr to apply following data manipulation steps to all the buffer data frames
purrr::map(
~.x %>%
# de-select columns for landscape data (e.g. those that have the prefix 'lc_')
select(!starts_with('lc_'))
)Now let’s explore the correlations between variables at each buffer scale and see what features we may need to remove or group together (if appropriate)
Correlation plots
Now we need to make correlation plots for each buffer width to see
what variables are correlated at a given spatial scale. We can use
purrr::map() with the chart.Correlation()
function from the PerformanceAnalytics package to make
correlation plots with a specified method (e.g., pearson, spearman,
etc.) That also show histograms and scatterplots of each variable.
Let’s do this for the anthropogenic data first
osm_anthro_df_2021_2022 %>%
purrr::map(
~.x %>%
# select only columns with covaraites not other info
select(harvest:osm_industrial) %>%
# use chart.correlation
chart.Correlation(.,
histogram = TRUE,
method = "pearson")
)
## $`250 meter buffer`
## NULL
##
## $`500 meter buffer`
## NULL
##
## $`750 meter buffer`
## NULL
##
## $`1000 meter buffer`
## NULL
##
## $`1250 meter buffer`
## NULL
##
## $`1500 meter buffer`
## NULL
##
## $`1750 meter buffer`
## NULL
##
## $`2000 meter buffer`
## NULL
##
## $`2250 meter buffer`
## NULL
##
## $`2500 meter buffer`
## NULL
##
## $`2750 meter buffer`
## NULL
##
## $`3000 meter buffer`
## NULL
##
## $`3250 meter buffer`
## NULL
##
## $`3500 meter buffer`
## NULL
##
## $`3750 meter buffer`
## NULL
##
## $`4000 meter buffer`
## NULL
##
## $`4250 meter buffer`
## NULL
##
## $`4500 meter buffer`
## NULL
##
## $`4750 meter buffer`
## NULL
##
## $`5000 meter buffer`
## NULLThis creates a lot of plots (1 for each buffer size) and I haven’t found a way to label them by the buffer size yet unfortunately. But hopefully there will be trends among the buffer sizes to simplify the process of choosing covariates
in Rstudio you can click on the white square icon in the upper right-hand corner of the figures to open a new window with the plots so you can see the values easier
In order of buffer size I’m going to summarize covariates that are correlated below with their respective r2 values. I’m only listing correlations above 0.6 for each buffer size
250m roads + veg edges 0.71 this is to be expected as veg edges occur along the sides of roads (primarily) as well as other disturbance features. We will choose roads in the analysis over veg edges
500m pipeline + transmission lines 0.63
we may be able to combine these roads + veg edges 0.73750m
pipeline + transmission lines 0.70
roads + veg edges 0.741000m
pipeline + transmission lines 0.73
roads + veg edges 0.791250m
pipeline + transmission lines 0.72
roads + veg edges 0.811500m
pipeline + transmission lines 0.72
roads + veg edges 0.84
roads + wells 0.641750m pipeline + transmission lines 0.72
roads + veg edges 0.85
roads + wells 0.702000m
pipeline + transmission lines 0.72
roads + veg edges 0.86
roads + wells 0.732250m
pipeline + transmission lines 0.71
roads + veg edges 0.87
roads + wells 0.762500m
pipleine + roads 0.61
pipeline + transmission lines 0.70
roads + veg edges 0.88
roads + wells 0.76
roads + osm_industrial 0.612750m
pipeline + roads 0.61
pipeline + transmission lines 0.70
roads + veg edges 0.88
roads + wells 0.76
roads + osm_industrial 0.613000m
pipeline + roads 0.63
pipeline + transmission lines 0.69
roads + veg edges 0.88
roads + wells 0.77
roads + osm_industrial 0.63
at this point there are some obvious trends between roads and several features and pipelines and transmission lines. for simplicity sake I am only going to report correlations for variables not correlated with roads, as we will have to drop this covariate, and new pairs of highly correlated variables. I will also not specify the buffer size if it’s found in more than one buffer
- 3250m +
veg edges + wells 0.61
pipeline + wells 0.67
pipeline + wells 0.69
In summary we should merge pipelines and transmission lines, remove roads, and remove veg edges from the analysis to insure we don’t majorly violate assumptions of independence for our models. We will re-assess correlations after making these changes.
List of final variables
I first pasted the full list here and then deleted or added combined
variables as I went through each buffer’s correlation plot
harvest
seismic lines
seismic lines 3D
trails
pipeline and transmission lines
wells
osm_industrial
Reformat data
We’ll reformat the anthropogenicsubset data here and re-run the correlation plots
osm_anthro_df_2021_2022 <- osm_anthro_df_2021_2022 %>%
# use purrr
purrr::map(
~.x %>%
# mutate data to combine pipeline and transmission line
mutate(pipeline_transmission_lines = (pipeline + transmission_lines)) %>%
# remove correlated variables we won't include in subset models
select(! c(pipeline, roads, veg_edges, transmission_lines)) %>%
# relocate column so new covariate is with others
relocate(pipeline_transmission_lines,
.after = wells)
)Re-run correlation plots
Now we should double check that this solves any issues of highly correlated variables by re-running the plots from before
osm_anthro_df_2021_2022 %>%
purrr::map(
~.x %>%
# select only columns with covariates not other info
select(harvest:osm_industrial) %>%
# use chart.correlation
chart.Correlation(.,
histogram = TRUE,
method = "pearson")
)
## $`250 meter buffer`
## NULL
##
## $`500 meter buffer`
## NULL
##
## $`750 meter buffer`
## NULL
##
## $`1000 meter buffer`
## NULL
##
## $`1250 meter buffer`
## NULL
##
## $`1500 meter buffer`
## NULL
##
## $`1750 meter buffer`
## NULL
##
## $`2000 meter buffer`
## NULL
##
## $`2250 meter buffer`
## NULL
##
## $`2500 meter buffer`
## NULL
##
## $`2750 meter buffer`
## NULL
##
## $`3000 meter buffer`
## NULL
##
## $`3250 meter buffer`
## NULL
##
## $`3500 meter buffer`
## NULL
##
## $`3750 meter buffer`
## NULL
##
## $`4000 meter buffer`
## NULL
##
## $`4250 meter buffer`
## NULL
##
## $`4500 meter buffer`
## NULL
##
## $`4750 meter buffer`
## NULL
##
## $`5000 meter buffer`
## NULLWells and the new pipelines and transmission lines variable seem to become increasingly correlated as the buffer size increases but the max r squared value is 0.66 so we will leave it in for now and discuss with others about whether or not to remove it since it’s on the cusp.
Landscape
Now let’s do the same for the landscape data
osm_landscape_df_2021_2022 <- osm_final_df_2021_2022 %>%
# use purr to apply following data manipulation steps to all the buffer data frames
purrr::map(
~.x %>%
# de-select columns for developed data
select(!harvest:wells &
!osm_industrial)
)Now we will follow the same process with the landscape data for generating correlation plots and assessing whaich variables need to be removed or combined
Correlation plots
osm_landscape_df_2021_2022 %>%
purrr::map(
~.x %>%
# select only columns with covariates not other info
select(lc_grassland:lc_shrub) %>%
# use chart.correlation
chart.Correlation(.,
histogram = TRUE,
method = "pearson")
)
## $`250 meter buffer`
## NULL
##
## $`500 meter buffer`
## NULL
##
## $`750 meter buffer`
## NULL
##
## $`1000 meter buffer`
## NULL
##
## $`1250 meter buffer`
## NULL
##
## $`1500 meter buffer`
## NULL
##
## $`1750 meter buffer`
## NULL
##
## $`2000 meter buffer`
## NULL
##
## $`2250 meter buffer`
## NULL
##
## $`2500 meter buffer`
## NULL
##
## $`2750 meter buffer`
## NULL
##
## $`3000 meter buffer`
## NULL
##
## $`3250 meter buffer`
## NULL
##
## $`3500 meter buffer`
## NULL
##
## $`3750 meter buffer`
## NULL
##
## $`4000 meter buffer`
## NULL
##
## $`4250 meter buffer`
## NULL
##
## $`4500 meter buffer`
## NULL
##
## $`4750 meter buffer`
## NULL
##
## $`5000 meter buffer`
## NULLBelow is a summary of the different buffer sizes and correlated variables
250m
None >0.6500m coniferous + broadleaf 0.63
750m
coniferous + broadleaf 0.661000m
coniferous + braodleaf 0.68
Coniferous and broadleaf appear to be the only variables highly correlated so I think what we will do is combine all 3 forest types into one category, because we really aren’t interested in interpreting these individual variables but rather seeing if there are differences in which buffer is the best fit between development and landscape features.
Reformat data
osm_landscape_df_2021_2022 <- osm_landscape_df_2021_2022 %>%
# use purrr to apply the functions to all data frames
purrr::map(
~.x %>%
# use mutate to combine forest types into one variable
mutate(lc_forest = lc_coniferous + lc_broadleaf + lc_mixed) %>%
# remove old variables
select(! c(lc_coniferous, lc_broadleaf, lc_mixed)) %>%
# relocate the new column with. the other covariates
relocate(lc_forest,
.after = buff_dist)
)Looks good! Now let’s re-run the correlation plots
Re-run correlation plots
osm_landscape_df_2021_2022 %>%
purrr::map(
~.x %>%
# select only columns with covariates not other info
select(lc_forest:lc_shrub) %>%
# use chart.correlation
chart.Correlation(.,
histogram = TRUE,
method = "pearson")
)
## $`250 meter buffer`
## NULL
##
## $`500 meter buffer`
## NULL
##
## $`750 meter buffer`
## NULL
##
## $`1000 meter buffer`
## NULL
##
## $`1250 meter buffer`
## NULL
##
## $`1500 meter buffer`
## NULL
##
## $`1750 meter buffer`
## NULL
##
## $`2000 meter buffer`
## NULL
##
## $`2250 meter buffer`
## NULL
##
## $`2500 meter buffer`
## NULL
##
## $`2750 meter buffer`
## NULL
##
## $`3000 meter buffer`
## NULL
##
## $`3250 meter buffer`
## NULL
##
## $`3500 meter buffer`
## NULL
##
## $`3750 meter buffer`
## NULL
##
## $`4000 meter buffer`
## NULL
##
## $`4250 meter buffer`
## NULL
##
## $`4500 meter buffer`
## NULL
##
## $`4750 meter buffer`
## NULL
##
## $`5000 meter buffer`
## NULLWell crap. Turns out the primary landscape types are forest and shrub so when we combine all three forest types we now have an issue with autocorrelation with the shrub landcover type. Might need to rethink the approach here.
For now will use one or the other
Subset Analysis
Now that the two separate data files are ready we can run an analysis with each, similar to what we did with the global models and see if this changes our findings from the global model or if there are differences in results between the two model subsets
Anthropogenic
We will do another analysis for each species as we did previously.
Black bear
bear_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI that aren't correlated
scale(harvest) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
bear_anthro_model.sel <- model.sel(bear_anthro_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
bear_anthro_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 4000 meter buffer -0.6015232 + 0.05541942
## 2 3750 meter buffer -0.6013650 + 0.05112073
## 3 3500 meter buffer -0.6006633 + 0.04790540
## 4 4250 meter buffer -0.6006769 + 0.06464311
## 5 3250 meter buffer -0.6003573 + 0.05221540
## 6 250 meter buffer -0.5947386 + 0.01448212
## 7 4500 meter buffer -0.6006909 + 0.06537875
## 8 4750 meter buffer -0.6004806 + 0.06567352
## 9 3000 meter buffer -0.5993263 + 0.05157993
## 10 2750 meter buffer -0.5986616 + 0.05077139
## 11 5000 meter buffer -0.5999002 + 0.06687890
## 12 2500 meter buffer -0.5977169 + 0.04560308
## 13 1750 meter buffer -0.5973738 + 0.04413308
## 14 2250 meter buffer -0.5970675 + 0.04059526
## 15 2000 meter buffer -0.5971866 + 0.04329195
## 16 1500 meter buffer -0.5970707 + 0.04253157
## 17 1000 meter buffer -0.5946072 + 0.02594788
## 18 1250 meter buffer -0.5956455 + 0.03523915
## 19 500 meter buffer -0.5940999 + 0.03240523
## 20 750 meter buffer -0.5926719 + 0.03607808
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 -0.138073054 -0.16132550
## 2 -0.139534500 -0.15194464
## 3 -0.125564825 -0.15595894
## 4 -0.132449593 -0.15004063
## 5 -0.114284284 -0.16171484
## 6 0.040705860 -0.07483023
## 7 -0.126194869 -0.14824552
## 8 -0.121661850 -0.14588034
## 9 -0.100714392 -0.16372884
## 10 -0.087575474 -0.16571955
## 11 -0.118360568 -0.14452364
## 12 -0.074669119 -0.15458549
## 13 -0.040286637 -0.14611903
## 14 -0.063582502 -0.14929281
## 15 -0.050383211 -0.14757252
## 16 -0.027985048 -0.13526727
## 17 -0.018972820 -0.12706675
## 18 -0.008794519 -0.12775540
## 19 0.011470208 -0.10182932
## 20 0.004609023 -0.10065339
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.08229338 -0.12772024 0.10905497
## 2 -0.08645588 -0.11545829 0.10791338
## 3 -0.08425655 -0.10015801 0.09872115
## 4 -0.08576510 -0.14671796 0.10781803
## 5 -0.08254602 -0.09434335 0.09166207
## 6 -0.13575399 -0.01958648 0.13376102
## 7 -0.08715346 -0.14886063 0.10723718
## 8 -0.08945536 -0.15039805 0.10538003
## 9 -0.08421909 -0.08098251 0.07872619
## 10 -0.08584863 -0.08104548 0.07847708
## 11 -0.09168571 -0.15026428 0.10128633
## 12 -0.08322838 -0.07426624 0.08318239
## 13 -0.09228563 -0.10733592 0.07712904
## 14 -0.08316945 -0.07224762 0.08658669
## 15 -0.08609534 -0.09301424 0.08296612
## 16 -0.09866618 -0.08144812 0.07839530
## 17 -0.11114535 -0.04636697 0.08467478
## 18 -0.10642820 -0.05029657 0.08329980
## 19 -0.12610340 -0.01986758 0.07753893
## 20 -0.11981538 -0.02425698 0.05208192
## cond(scale(wells)) df logLik AICc delta weight
## 1 0.210212826 9 -452.8033 924.4174 0.0000000 0.174063282
## 2 0.194585969 9 -452.8904 924.5917 0.1742734 0.159538017
## 3 0.174881985 9 -453.4032 925.6171 1.1996825 0.095543120
## 4 0.214459814 9 -453.5006 925.8119 1.3944910 0.086675686
## 5 0.172225184 9 -453.5363 925.8834 1.4660072 0.083631090
## 6 -0.007107041 9 -453.6497 926.1102 1.6927697 0.074666691
## 7 0.214946235 9 -453.7219 926.2546 1.8371651 0.069465927
## 8 0.211261958 9 -454.0837 926.9781 2.5607052 0.048379025
## 9 0.162990824 9 -454.2466 927.3040 2.8866041 0.041104471
## 10 0.157018861 9 -454.3500 927.5108 3.0934027 0.037066647
## 11 0.210985374 9 -454.4272 927.6652 3.2477757 0.034313233
## 12 0.130950427 9 -454.9214 928.6536 4.2361253 0.020933615
## 13 0.098847376 9 -455.1992 929.2093 4.7918653 0.015855022
## 14 0.119175813 9 -455.3339 929.4786 5.0611217 0.013857936
## 15 0.105368769 9 -455.3362 929.4833 5.0658284 0.013825362
## 16 0.082659690 9 -455.7318 930.2743 5.8569115 0.009308828
## 17 0.066083924 9 -455.9711 930.7531 6.3356474 0.007327213
## 18 0.063730888 9 -456.0551 930.9210 6.5035624 0.006737155
## 19 0.080648560 9 -456.2005 931.2118 6.7943782 0.005825412
## 20 0.083262566 9 -457.3303 933.4713 9.0538827 0.001882268
## Dataset
## 1 osm_anthro_df_2021_2022
## 2 osm_anthro_df_2021_2022
## 3 osm_anthro_df_2021_2022
## 4 osm_anthro_df_2021_2022
## 5 osm_anthro_df_2021_2022
## 6 osm_anthro_df_2021_2022
## 7 osm_anthro_df_2021_2022
## 8 osm_anthro_df_2021_2022
## 9 osm_anthro_df_2021_2022
## 10 osm_anthro_df_2021_2022
## 11 osm_anthro_df_2021_2022
## 12 osm_anthro_df_2021_2022
## 13 osm_anthro_df_2021_2022
## 14 osm_anthro_df_2021_2022
## 15 osm_anthro_df_2021_2022
## 16 osm_anthro_df_2021_2022
## 17 osm_anthro_df_2021_2022
## 18 osm_anthro_df_2021_2022
## 19 osm_anthro_df_2021_2022
## 20 osm_anthro_df_2021_2022We will use the model.sel() function from the
MuMIn package to compare the global models for each buffer
width and see which buffer fits the black bear data best
model.sel(bear_anthro_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(osm_ind))
## 4000 meter buffer -0.6015 + 0.05542 -0.138100
## 3750 meter buffer -0.6014 + 0.05112 -0.139500
## 3500 meter buffer -0.6007 + 0.04791 -0.125600
## 4250 meter buffer -0.6007 + 0.06464 -0.132400
## 3250 meter buffer -0.6004 + 0.05222 -0.114300
## 250 meter buffer -0.5947 + 0.01448 0.040710
## 4500 meter buffer -0.6007 + 0.06538 -0.126200
## 4750 meter buffer -0.6005 + 0.06567 -0.121700
## 3000 meter buffer -0.5993 + 0.05158 -0.100700
## 2750 meter buffer -0.5987 + 0.05077 -0.087580
## 5000 meter buffer -0.5999 + 0.06688 -0.118400
## 2500 meter buffer -0.5977 + 0.04560 -0.074670
## 1750 meter buffer -0.5974 + 0.04413 -0.040290
## 2250 meter buffer -0.5971 + 0.04060 -0.063580
## 2000 meter buffer -0.5972 + 0.04329 -0.050380
## 1500 meter buffer -0.5971 + 0.04253 -0.027990
## 1000 meter buffer -0.5946 + 0.02595 -0.018970
## 1250 meter buffer -0.5956 + 0.03524 -0.008795
## 500 meter buffer -0.5941 + 0.03241 0.011470
## 750 meter buffer -0.5927 + 0.03608 0.004609
## cnd(scl(ppl_trn_lns)) cnd(scl(ssm_lns_3D)) cnd(scl(ssm_lns))
## 4000 meter buffer -0.16130 -0.08229 -0.12770
## 3750 meter buffer -0.15190 -0.08646 -0.11550
## 3500 meter buffer -0.15600 -0.08426 -0.10020
## 4250 meter buffer -0.15000 -0.08577 -0.14670
## 3250 meter buffer -0.16170 -0.08255 -0.09434
## 250 meter buffer -0.07483 -0.13580 -0.01959
## 4500 meter buffer -0.14820 -0.08715 -0.14890
## 4750 meter buffer -0.14590 -0.08946 -0.15040
## 3000 meter buffer -0.16370 -0.08422 -0.08098
## 2750 meter buffer -0.16570 -0.08585 -0.08105
## 5000 meter buffer -0.14450 -0.09169 -0.15030
## 2500 meter buffer -0.15460 -0.08323 -0.07427
## 1750 meter buffer -0.14610 -0.09229 -0.10730
## 2250 meter buffer -0.14930 -0.08317 -0.07225
## 2000 meter buffer -0.14760 -0.08610 -0.09301
## 1500 meter buffer -0.13530 -0.09867 -0.08145
## 1000 meter buffer -0.12710 -0.11110 -0.04637
## 1250 meter buffer -0.12780 -0.10640 -0.05030
## 500 meter buffer -0.10180 -0.12610 -0.01987
## 750 meter buffer -0.10070 -0.11980 -0.02426
## cnd(scl(trl)) cnd(scl(wll)) df logLik AICc delta weight
## 4000 meter buffer 0.10910 0.210200 9 -452.803 924.4 0.00 0.174
## 3750 meter buffer 0.10790 0.194600 9 -452.890 924.6 0.17 0.160
## 3500 meter buffer 0.09872 0.174900 9 -453.403 925.6 1.20 0.096
## 4250 meter buffer 0.10780 0.214500 9 -453.501 925.8 1.39 0.087
## 3250 meter buffer 0.09166 0.172200 9 -453.536 925.9 1.47 0.084
## 250 meter buffer 0.13380 -0.007107 9 -453.650 926.1 1.69 0.075
## 4500 meter buffer 0.10720 0.214900 9 -453.722 926.3 1.84 0.069
## 4750 meter buffer 0.10540 0.211300 9 -454.084 927.0 2.56 0.048
## 3000 meter buffer 0.07873 0.163000 9 -454.247 927.3 2.89 0.041
## 2750 meter buffer 0.07848 0.157000 9 -454.350 927.5 3.09 0.037
## 5000 meter buffer 0.10130 0.211000 9 -454.427 927.7 3.25 0.034
## 2500 meter buffer 0.08318 0.131000 9 -454.921 928.7 4.24 0.021
## 1750 meter buffer 0.07713 0.098850 9 -455.199 929.2 4.79 0.016
## 2250 meter buffer 0.08659 0.119200 9 -455.334 929.5 5.06 0.014
## 2000 meter buffer 0.08297 0.105400 9 -455.336 929.5 5.07 0.014
## 1500 meter buffer 0.07840 0.082660 9 -455.732 930.3 5.86 0.009
## 1000 meter buffer 0.08467 0.066080 9 -455.971 930.8 6.34 0.007
## 1250 meter buffer 0.08330 0.063730 9 -456.055 930.9 6.50 0.007
## 500 meter buffer 0.07754 0.080650 9 -456.200 931.2 6.79 0.006
## 750 meter buffer 0.05208 0.083260 9 -457.330 933.5 9.05 0.002
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)This is quite a bit different from the global results which indicated that the 250m buffer was best fit for black bears, whereas this one it is the 4000m buffer
Top model summary
summary(bear_anthro_mods$`4000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(black_bear, absent_black_bear) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 923.6 954.6 -452.8 905.6 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.003319 0.05762
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.60152 0.05278 -11.398 <2e-16 ***
## scale(harvest) 0.05542 0.06258 0.886 0.3759
## scale(seismic_lines) -0.12772 0.08033 -1.590 0.1119
## scale(seismic_lines_3D) -0.08229 0.06340 -1.298 0.1943
## scale(trails) 0.10905 0.05301 2.057 0.0397 *
## scale(wells) 0.21021 0.09903 2.123 0.0338 *
## scale(osm_industrial) -0.13807 0.06539 -2.112 0.0347 *
## scale(pipeline_transmission_lines) -0.16133 0.07616 -2.118 0.0342 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quck test on scaling
Going to run the models again WITHOUT scaling variables as recent literature suggests this may influence model results.
test_bear_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI that aren't correlated
harvest +
seismic_lines +
seismic_lines_3D +
trails +
wells +
osm_industrial +
pipeline_transmission_lines +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')model.sel(test_bear_anthro_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(hrv) cnd(osm_ind) cnd(ppl_trn_lns)
## 3750 meter buffer -0.3935 + 0.7610 -4.06200 -8.432
## 3500 meter buffer -0.4274 + 0.5648 -3.46600 -8.004
## 4250 meter buffer -0.3758 + 0.8226 -3.83500 -8.574
## 3250 meter buffer -0.4354 + 0.5984 -3.08000 -8.013
## 250 meter buffer -0.5559 + 0.0956 0.40430 -1.096
## 4500 meter buffer -0.3724 + 0.8539 -3.66900 -8.731
## 4750 meter buffer -0.3672 + 0.8794 -3.55700 -8.858
## 3000 meter buffer -0.4453 + 0.5780 -2.64100 -7.755
## 2750 meter buffer -0.4431 + 0.5598 -2.27400 -7.557
## 5000 meter buffer -0.3637 + 0.9151 -3.47500 -9.010
## 2500 meter buffer -0.4561 + 0.4954 -1.96300 -6.772
## 1750 meter buffer -0.4044 + 0.4531 -1.05300 -5.135
## 2250 meter buffer -0.4645 + 0.4344 -1.67800 -6.143
## 2000 meter buffer -0.4287 + 0.4542 -1.32400 -5.640
## 1500 meter buffer -0.4506 + 0.4292 -0.70590 -4.310
## 1000 meter buffer -0.4979 + 0.2331 -0.40810 -3.166
## 1250 meter buffer -0.4983 + 0.3435 -0.21260 -3.680
## 500 meter buffer -0.5637 + 0.2399 0.15480 -1.773
## 750 meter buffer -0.5451 + 0.2963 0.07942 -2.118
## 4000 meter buffer -0.3865 + 0.7698 -4.03600 -9.096
## cnd(ssm_lns) cnd(ssm_lns_3D) cnd(trl) cnd(wll) df logLik
## 3750 meter buffer -37.410 -7.630 80.15 12.3800 9 -452.958
## 3500 meter buffer -27.100 -7.226 71.71 9.5940 9 -453.403
## 4250 meter buffer -41.040 -7.557 82.40 12.5900 9 -453.501
## 3250 meter buffer -25.080 -7.046 65.78 9.2670 9 -453.536
## 250 meter buffer -2.274 -10.600 21.61 -0.1549 9 -453.650
## 4500 meter buffer -42.150 -7.784 83.98 12.9400 9 -453.722
## 4750 meter buffer -42.870 -8.093 84.71 13.1000 9 -454.084
## 3000 meter buffer -21.300 -7.146 55.37 8.5810 9 -454.247
## 2750 meter buffer -21.250 -7.255 53.48 8.0170 9 -454.350
## 5000 meter buffer -43.150 -8.404 83.25 13.3700 9 -454.427
## 2500 meter buffer -19.300 -7.010 54.05 6.4690 9 -454.921
## 1750 meter buffer -26.160 -7.751 41.56 4.4000 9 -455.199
## 2250 meter buffer -18.560 -6.970 54.29 5.7130 9 -455.334
## 2000 meter buffer -23.350 -7.220 48.93 4.8850 9 -455.336
## 1500 meter buffer -19.110 -8.372 37.74 3.5080 9 -455.732
## 1000 meter buffer -10.280 -9.509 33.28 2.4940 9 -455.971
## 1250 meter buffer -11.420 -9.124 36.02 2.6370 9 -456.055
## 500 meter buffer -3.477 -11.040 20.00 2.3810 9 -456.200
## 750 meter buffer -4.862 -10.420 17.61 2.8880 9 -457.330
## 4000 meter buffer -38.440 -7.300 81.98 12.9300 9
## AICc delta
## 3750 meter buffer 924.7 0.00
## 3500 meter buffer 925.6 0.89
## 4250 meter buffer 925.8 1.09
## 3250 meter buffer 925.9 1.16
## 250 meter buffer 926.1 1.38
## 4500 meter buffer 926.3 1.53
## 4750 meter buffer 927.0 2.25
## 3000 meter buffer 927.3 2.58
## 2750 meter buffer 927.5 2.78
## 5000 meter buffer 927.7 2.94
## 2500 meter buffer 928.7 3.93
## 1750 meter buffer 929.2 4.48
## 2250 meter buffer 929.5 4.75
## 2000 meter buffer 929.5 4.76
## 1500 meter buffer 930.3 5.55
## 1000 meter buffer 930.8 6.03
## 1250 meter buffer 930.9 6.19
## 500 meter buffer 931.2 6.49
## 750 meter buffer 933.5 8.74
## 4000 meter buffer
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)Interesting, you get almost the same results but the 4000m buffer is the worst fit. There was a warning about model convergence issues which I suspect is the problem, this is fairly common with unscaled data. We will proceed with scaling the data as is standard practice still, but consider in discussion on ms how this may affect results.
Caribou
caribou_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(caribou, absent_caribou) ~
# HFI that aren't correlated
scale(harvest) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')# run model selection and save the results as a tibble for graphing use later
caribou_anthro_model.sel <- model.sel(caribou_anthro_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
caribou_anthro_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 1000 meter buffer -5.548202 + -0.7434272
## 2 1250 meter buffer -5.414419 + -0.3827273
## 3 750 meter buffer -5.861136 + -1.8466582
## 4 1500 meter buffer -5.316414 + -0.1687568
## 5 2000 meter buffer -5.302913 + -0.1953843
## 6 2500 meter buffer -5.357304 + -0.1833198
## 7 2750 meter buffer -5.356840 + -0.1653046
## 8 500 meter buffer -5.731118 + -2.2269204
## 9 1750 meter buffer -5.267118 + -0.1693908
## 10 2250 meter buffer -5.286203 + -0.1932869
## 11 3000 meter buffer -5.325367 + -0.1264651
## 12 3250 meter buffer -5.269542 + -0.1375689
## 13 3500 meter buffer -5.222246 + -0.2107347
## 14 250 meter buffer -205.478643 + -603.1262423
## 15 3750 meter buffer -5.176264 + -0.3112598
## 16 5000 meter buffer -5.112732 + -1.1953009
## 17 4750 meter buffer -5.094802 + -0.9945439
## 18 4000 meter buffer -5.124358 + -0.4164396
## 19 4250 meter buffer -5.081197 + -0.5981531
## 20 4500 meter buffer -5.069085 + -0.8296411
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 -1.28956739 -0.3131129
## 2 -1.25756529 -0.3097483
## 3 -1.09329936 -0.2940728
## 4 -1.08717216 -0.4410655
## 5 -1.32940469 -0.5624233
## 6 -0.98599062 -0.9072482
## 7 -0.65443205 -1.0623980
## 8 -0.35599956 -0.2392163
## 9 -1.20635579 -0.4705007
## 10 -1.17561085 -0.6466125
## 11 -0.52741655 -1.1040122
## 12 -0.45679026 -1.0464903
## 13 -0.35285436 -1.0622802
## 14 -0.26819231 -0.4221266
## 15 -0.29414398 -1.0294607
## 16 0.02831963 -0.9535882
## 17 0.01072871 -0.9732911
## 18 -0.21849650 -0.9482116
## 19 -0.13794582 -0.9275871
## 20 -0.06007709 -0.9202266
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.18749404 0.15459501 0.03898567
## 2 -0.19535612 0.21040880 0.02258331
## 3 -0.12132902 0.22122254 0.06621093
## 4 -0.21181586 0.14586261 -0.12468209
## 5 -0.21414456 0.14382342 -0.15185239
## 6 -0.20674375 0.15923967 -0.25533265
## 7 -0.18116466 0.10498796 -0.30340030
## 8 -0.12408563 0.20969474 -0.03229518
## 9 -0.19671517 0.09651472 -0.13096070
## 10 -0.22651171 0.17175204 -0.17320021
## 11 -0.17061300 0.02473656 -0.39107391
## 12 -0.18673037 -0.03486376 -0.44815026
## 13 -0.13735234 -0.04570776 -0.48144176
## 14 -0.17968245 0.21911311 -0.26721143
## 15 -0.10411236 -0.08400064 -0.45833715
## 16 0.03660485 -0.03071875 -0.54129254
## 17 0.01761321 -0.04810542 -0.53944909
## 18 -0.07631351 -0.10206367 -0.40605803
## 19 -0.02956579 -0.08693773 -0.42725566
## 20 0.00300806 -0.07011857 -0.45824025
## cond(scale(wells)) df logLik AICc delta weight
## 1 0.6921299 9 -133.0946 285.0001 0.000000 8.840982e-01
## 2 0.7364424 9 -135.6750 290.1607 5.160649 6.696993e-02
## 3 0.6323499 9 -136.4419 291.6947 6.694596 3.110194e-02
## 4 0.8065110 9 -138.0477 294.9062 9.906126 6.243276e-03
## 5 0.9663138 9 -139.0421 296.8950 11.894887 2.309717e-03
## 6 1.2408913 9 -139.1457 297.1021 12.102056 2.082440e-03
## 7 1.2757101 9 -139.2793 297.3695 12.369416 1.821864e-03
## 8 0.5188765 9 -139.3748 297.5605 12.560392 1.655946e-03
## 9 0.8359218 9 -139.4659 297.7427 12.742568 1.511775e-03
## 10 1.0391623 9 -139.9962 298.8032 13.803158 8.895756e-04
## 11 1.2519807 9 -140.1523 299.1154 14.115343 7.610141e-04
## 12 1.1743808 9 -141.5352 301.8812 16.881131 1.909015e-04
## 13 1.1335767 9 -142.0319 302.8746 17.874512 1.161715e-04
## 14 0.3501608 9 -142.1019 303.0146 18.014554 1.083153e-04
## 15 1.0852318 9 -142.6347 304.0802 19.080079 6.357905e-05
## 16 0.8192121 9 -143.7337 306.2782 21.278124 2.118433e-05
## 17 0.8414263 9 -143.8595 306.5297 21.529651 1.868083e-05
## 18 0.9949019 9 -143.9159 306.6426 21.642557 1.765546e-05
## 19 0.9080571 9 -144.5263 307.8633 22.863238 9.589840e-06
## 20 0.8303706 9 -144.6750 308.1608 23.160747 8.264341e-06
## Dataset
## 1 osm_anthro_df_2021_2022
## 2 osm_anthro_df_2021_2022
## 3 osm_anthro_df_2021_2022
## 4 osm_anthro_df_2021_2022
## 5 osm_anthro_df_2021_2022
## 6 osm_anthro_df_2021_2022
## 7 osm_anthro_df_2021_2022
## 8 osm_anthro_df_2021_2022
## 9 osm_anthro_df_2021_2022
## 10 osm_anthro_df_2021_2022
## 11 osm_anthro_df_2021_2022
## 12 osm_anthro_df_2021_2022
## 13 osm_anthro_df_2021_2022
## 14 osm_anthro_df_2021_2022
## 15 osm_anthro_df_2021_2022
## 16 osm_anthro_df_2021_2022
## 17 osm_anthro_df_2021_2022
## 18 osm_anthro_df_2021_2022
## 19 osm_anthro_df_2021_2022
## 20 osm_anthro_df_2021_2022Let’s look at the summary for the top model
summary(caribou_anthro_mods$`1000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(caribou, absent_caribou) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 284.2 315.2 -133.1 266.2 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 4.648 2.156
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.54820 1.05969 -5.236 1.64e-07 ***
## scale(harvest) -0.74343 0.66903 -1.111 0.2665
## scale(seismic_lines) 0.15460 0.16295 0.949 0.3428
## scale(seismic_lines_3D) -0.18749 0.19623 -0.955 0.3393
## scale(trails) 0.03899 0.20948 0.186 0.8524
## scale(wells) 0.69213 0.15941 4.342 1.41e-05 ***
## scale(osm_industrial) -1.28957 0.45284 -2.848 0.0044 **
## scale(pipeline_transmission_lines) -0.31311 0.28058 -1.116 0.2645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Coyote
coyote_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(coyote, absent_coyote) ~
# HFI that aren't correlated
scale(harvest) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
coyote_anthro_model.sel <- model.sel(coyote_anthro_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
coyote_anthro_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 4750 meter buffer -1.392504 + -0.168455867
## 2 5000 meter buffer -1.394096 + -0.178552842
## 3 4500 meter buffer -1.390544 + -0.149430735
## 4 3000 meter buffer -1.381542 + -0.073710735
## 5 2750 meter buffer -1.380048 + -0.067891675
## 6 4250 meter buffer -1.388380 + -0.132438631
## 7 3250 meter buffer -1.382392 + -0.082540861
## 8 2250 meter buffer -1.378863 + -0.049801658
## 9 2500 meter buffer -1.379342 + -0.057112417
## 10 4000 meter buffer -1.386264 + -0.124148068
## 11 2000 meter buffer -1.378030 + -0.034052913
## 12 1750 meter buffer -1.379266 + -0.017100267
## 13 3500 meter buffer -1.383006 + -0.099848855
## 14 3750 meter buffer -1.384199 + -0.110751481
## 15 1500 meter buffer -1.381316 + -0.004779912
## 16 1250 meter buffer -1.382910 + -0.009979531
## 17 750 meter buffer -1.387961 + 0.019511213
## 18 1000 meter buffer -1.385038 + -0.013772602
## 19 500 meter buffer -1.387937 + 0.008353305
## 20 250 meter buffer -1.376811 + -0.091722768
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 0.4460095 0.0492885780
## 2 0.4403746 0.0554699985
## 3 0.4497606 0.0288879296
## 4 0.4563655 -0.0458324383
## 5 0.4562499 -0.0360959773
## 6 0.4488958 0.0122997745
## 7 0.4497229 -0.0367862183
## 8 0.4118956 0.0258917111
## 9 0.4265502 0.0004534265
## 10 0.4384295 -0.0152152021
## 11 0.4143112 0.0341437667
## 12 0.4133428 0.0524894279
## 13 0.4311573 -0.0254287541
## 14 0.4269129 -0.0200063062
## 15 0.4113818 0.0590109789
## 16 0.3993337 0.0761563708
## 17 0.3722183 0.1323948034
## 18 0.3735062 0.1117975234
## 19 0.3456310 0.0713110747
## 20 0.2663284 0.1284211047
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.05030671 0.1597048 0.021534287
## 2 -0.04633720 0.1629008 0.030254427
## 3 -0.04988250 0.1557877 0.011283848
## 4 -0.05317002 0.1214172 0.020233072
## 5 -0.06249270 0.1267952 0.018038495
## 6 -0.05041775 0.1395795 0.004724674
## 7 -0.04979732 0.1167598 0.013729476
## 8 -0.10057216 0.1737527 0.058867307
## 9 -0.07836984 0.1647012 0.040199115
## 10 -0.04670352 0.1303833 0.009784945
## 11 -0.12410064 0.1496142 0.050715263
## 12 -0.14991651 0.1417368 0.049734431
## 13 -0.04930477 0.1189339 0.021716258
## 14 -0.04822736 0.1191147 0.020433831
## 15 -0.17358676 0.1518348 0.038895595
## 16 -0.18776428 0.1577051 0.018111061
## 17 -0.15778008 0.1663454 -0.073215761
## 18 -0.16523423 0.1549719 0.002132869
## 19 -0.12698933 0.1540626 -0.113309983
## 20 -0.05136722 0.1033641 0.008134052
## cond(scale(wells)) df logLik AICc delta weight
## 1 0.15121332 9 -478.8307 976.4721 0.000000 2.677073e-01
## 2 0.15506108 9 -478.9603 976.7314 0.259249 2.351609e-01
## 3 0.16170342 9 -479.3838 977.5784 1.106249 1.539719e-01
## 4 0.22806529 9 -479.6742 978.1592 1.687030 1.151666e-01
## 5 0.22267665 9 -480.4034 979.6175 3.145408 5.554482e-02
## 6 0.16963788 9 -480.4602 979.7312 3.259061 5.247642e-02
## 7 0.21790659 9 -480.6905 980.1918 3.719705 4.168086e-02
## 8 0.24090134 9 -481.6381 982.0870 5.614863 1.615877e-02
## 9 0.23514655 9 -481.6522 982.1152 5.643089 1.593232e-02
## 10 0.19206688 9 -481.8575 982.5257 6.053585 1.297601e-02
## 11 0.23595126 9 -482.0702 982.9512 6.479104 1.048916e-02
## 12 0.23854223 9 -482.3840 983.5788 7.106717 7.664015e-03
## 13 0.21732776 9 -482.4476 983.7060 7.233908 7.191793e-03
## 14 0.21147612 9 -482.9246 984.6600 8.187889 4.463575e-03
## 15 0.25465521 9 -483.2020 985.2148 8.742685 3.382287e-03
## 16 0.23525786 9 -487.8267 994.4643 17.992175 3.316723e-05
## 17 0.28845742 9 -493.6053 1006.0213 29.549198 1.025967e-07
## 18 0.24339094 9 -493.9776 1006.7660 30.293868 7.070178e-08
## 19 0.24886776 9 -499.5576 1017.9260 41.453864 2.667277e-10
## 20 0.03331044 9 -516.5012 1051.8131 75.340993 1.168346e-17
## Dataset
## 1 osm_anthro_df_2021_2022
## 2 osm_anthro_df_2021_2022
## 3 osm_anthro_df_2021_2022
## 4 osm_anthro_df_2021_2022
## 5 osm_anthro_df_2021_2022
## 6 osm_anthro_df_2021_2022
## 7 osm_anthro_df_2021_2022
## 8 osm_anthro_df_2021_2022
## 9 osm_anthro_df_2021_2022
## 10 osm_anthro_df_2021_2022
## 11 osm_anthro_df_2021_2022
## 12 osm_anthro_df_2021_2022
## 13 osm_anthro_df_2021_2022
## 14 osm_anthro_df_2021_2022
## 15 osm_anthro_df_2021_2022
## 16 osm_anthro_df_2021_2022
## 17 osm_anthro_df_2021_2022
## 18 osm_anthro_df_2021_2022
## 19 osm_anthro_df_2021_2022
## 20 osm_anthro_df_2021_2022summary(coyote_anthro_mods$`4750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(coyote, absent_coyote) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 975.7 1006.7 -478.8 957.7 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.3289 0.5735
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.39250 0.24038 -5.793 6.92e-09 ***
## scale(harvest) -0.16846 0.09498 -1.774 0.0761 .
## scale(seismic_lines) 0.15970 0.07739 2.064 0.0391 *
## scale(seismic_lines_3D) -0.05031 0.07036 -0.715 0.4746
## scale(trails) 0.02153 0.06545 0.329 0.7421
## scale(wells) 0.15121 0.08582 1.762 0.0781 .
## scale(osm_industrial) 0.44601 0.06039 7.386 1.51e-13 ***
## scale(pipeline_transmission_lines) 0.04929 0.08745 0.564 0.5730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fisher
fisher_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(fisher, absent_fisher) ~
# HFI that aren't correlated
scale(harvest) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
fisher_anthro_model.sel <- model.sel(fisher_anthro_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
fisher_anthro_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 250 meter buffer -2.970098 + -0.37281000
## 2 500 meter buffer -2.968812 + -0.32480059
## 3 2500 meter buffer -2.951236 + -0.06195582
## 4 750 meter buffer -2.953649 + -0.25235733
## 5 2750 meter buffer -2.947957 + -0.05096300
## 6 3000 meter buffer -2.942918 + -0.04421143
## 7 3250 meter buffer -2.940482 + -0.06051081
## 8 1000 meter buffer -2.956293 + -0.24611026
## 9 1250 meter buffer -2.959535 + -0.26157041
## 10 4500 meter buffer -2.953143 + -0.14596094
## 11 4750 meter buffer -2.957786 + -0.17650433
## 12 5000 meter buffer -2.960696 + -0.20226078
## 13 4250 meter buffer -2.947593 + -0.12067896
## 14 4000 meter buffer -2.942035 + -0.10750579
## 15 2250 meter buffer -2.946490 + -0.09139343
## 16 3500 meter buffer -2.937845 + -0.08977742
## 17 3750 meter buffer -2.939503 + -0.10755446
## 18 2000 meter buffer -2.944134 + -0.12836671
## 19 1500 meter buffer -2.951164 + -0.21612239
## 20 1750 meter buffer -2.947460 + -0.17881373
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 -0.261515339 -0.1093808
## 2 -0.235679393 -0.2236027
## 3 0.058014363 -0.3525791
## 4 -0.099587887 -0.2419767
## 5 0.075523849 -0.3390423
## 6 0.080274665 -0.3117343
## 7 0.044189029 -0.2765482
## 8 -0.034303787 -0.2149241
## 9 -0.018855638 -0.1834547
## 10 -0.238014725 -0.1913936
## 11 -0.290950939 -0.1599280
## 12 -0.321410734 -0.1182718
## 13 -0.176119696 -0.2046227
## 14 -0.085537745 -0.2241321
## 15 0.066759523 -0.3130683
## 16 0.002052139 -0.2189673
## 17 -0.040318968 -0.2047163
## 18 0.059915768 -0.2581474
## 19 0.028076185 -0.1903796
## 20 0.063695038 -0.2025935
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.1563917 -0.05936497 -0.0042085333
## 2 -0.2158947 -0.05955326 0.0046489457
## 3 -0.2064242 -0.12605138 -0.0271136892
## 4 -0.2619768 -0.02602934 -0.0096097628
## 5 -0.1925482 -0.15164212 -0.0487836831
## 6 -0.1849352 -0.14381325 -0.0692987305
## 7 -0.1894938 -0.15018407 -0.0786455578
## 8 -0.3244353 -0.03178542 0.0784057208
## 9 -0.3590342 -0.07089473 0.0780837981
## 10 -0.2182959 -0.12833971 0.0268554192
## 11 -0.2316126 -0.11391831 0.0639452090
## 12 -0.2549898 -0.11199744 0.0840044947
## 13 -0.2153749 -0.13468010 -0.0005200369
## 14 -0.2091631 -0.12611769 -0.0089314620
## 15 -0.2203454 -0.09884631 -0.0336466982
## 16 -0.1962564 -0.15263131 -0.0332706835
## 17 -0.2081682 -0.14087529 -0.0043050036
## 18 -0.2525273 -0.10545942 -0.0233358596
## 19 -0.3316512 -0.07759202 0.0611248729
## 20 -0.2944688 -0.09131610 0.0420946737
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.1090989 9 -282.4616 583.7339 0.0000000 0.394989486
## 2 0.1248778 9 -282.6540 584.1187 0.3848032 0.325856641
## 3 0.4010348 9 -284.7050 588.2207 4.4868056 0.041907145
## 4 0.1641634 9 -284.9030 588.6169 4.8829771 0.034376411
## 5 0.3795159 9 -285.0007 588.8121 5.0782177 0.031179172
## 6 0.3748553 9 -285.4104 589.6316 5.8977266 0.020697146
## 7 0.3791443 9 -285.4133 589.6375 5.9035591 0.020636876
## 8 0.1735040 9 -285.5955 590.0018 6.2678730 0.017200228
## 9 0.2060493 9 -285.7685 590.3478 6.6139213 0.014467409
## 10 0.4651678 9 -285.8575 590.5257 6.7918106 0.013236178
## 11 0.4716751 9 -285.8599 590.5307 6.7967645 0.013203433
## 12 0.4532900 9 -285.9048 590.6205 6.8865488 0.012623811
## 13 0.4438610 9 -285.9627 590.7361 7.0022011 0.011914529
## 14 0.4188396 9 -286.1362 591.0833 7.3493725 0.010015891
## 15 0.3562711 9 -286.1864 591.1836 7.4496634 0.009526024
## 16 0.3558264 9 -286.2814 591.3737 7.6397943 0.008662142
## 17 0.3783061 9 -286.2829 591.3767 7.6427417 0.008649386
## 18 0.3050207 9 -286.9352 592.6813 8.9473658 0.004504948
## 19 0.2194890 9 -287.1646 593.1400 9.4060500 0.003581688
## 20 0.2365684 9 -287.4210 593.6529 9.9189729 0.002771456
## Dataset
## 1 osm_anthro_df_2021_2022
## 2 osm_anthro_df_2021_2022
## 3 osm_anthro_df_2021_2022
## 4 osm_anthro_df_2021_2022
## 5 osm_anthro_df_2021_2022
## 6 osm_anthro_df_2021_2022
## 7 osm_anthro_df_2021_2022
## 8 osm_anthro_df_2021_2022
## 9 osm_anthro_df_2021_2022
## 10 osm_anthro_df_2021_2022
## 11 osm_anthro_df_2021_2022
## 12 osm_anthro_df_2021_2022
## 13 osm_anthro_df_2021_2022
## 14 osm_anthro_df_2021_2022
## 15 osm_anthro_df_2021_2022
## 16 osm_anthro_df_2021_2022
## 17 osm_anthro_df_2021_2022
## 18 osm_anthro_df_2021_2022
## 19 osm_anthro_df_2021_2022
## 20 osm_anthro_df_2021_2022summary(fisher_anthro_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(fisher, absent_fisher) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 582.9 613.9 -282.5 564.9 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.7269 0.8526
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.970098 0.365121 -8.135 4.13e-16 ***
## scale(harvest) -0.372810 0.115254 -3.235 0.00122 **
## scale(seismic_lines) -0.059365 0.081191 -0.731 0.46467
## scale(seismic_lines_3D) -0.156392 0.127570 -1.226 0.22023
## scale(trails) -0.004209 0.091425 -0.046 0.96328
## scale(wells) -0.109099 0.081448 -1.339 0.18041
## scale(osm_industrial) -0.261515 0.119236 -2.193 0.02829 *
## scale(pipeline_transmission_lines) -0.109381 0.130572 -0.838 0.40220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Grey wolf
wolf_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(grey_wolf, absent_grey_wolf) ~
# HFI that aren't correlated
scale(harvest) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
wolf_anthro_model.sel <- model.sel(wolf_anthro_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
wolf_anthro_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 2000 meter buffer -3.220596 + 0.103368649
## 2 2250 meter buffer -3.225892 + 0.124362457
## 3 1750 meter buffer -3.211318 + 0.084437026
## 4 2500 meter buffer -3.223418 + 0.143458198
## 5 1500 meter buffer -3.193621 + 0.072583037
## 6 2750 meter buffer -3.213796 + 0.164955175
## 7 3000 meter buffer -3.202014 + 0.160254805
## 8 1250 meter buffer -3.175858 + 0.072686636
## 9 3250 meter buffer -3.193875 + 0.153819497
## 10 3500 meter buffer -3.181859 + 0.148471242
## 11 3750 meter buffer -3.174576 + 0.138642186
## 12 1000 meter buffer -3.167931 + 0.063166488
## 13 4000 meter buffer -3.168011 + 0.132027971
## 14 4250 meter buffer -3.162301 + 0.103950000
## 15 4500 meter buffer -3.155558 + 0.085337746
## 16 4750 meter buffer -3.149556 + 0.069320656
## 17 500 meter buffer -3.154998 + -0.005899598
## 18 250 meter buffer -3.196617 + 0.031519912
## 19 5000 meter buffer -3.144870 + 0.052728101
## 20 750 meter buffer -3.145970 + 0.070424917
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 -0.32843085 0.11513789
## 2 -0.29216457 0.11632821
## 3 -0.29027507 0.08698004
## 4 -0.23782864 0.11847349
## 5 -0.27173295 0.05125234
## 6 -0.19798364 0.09800387
## 7 -0.19161392 0.09266036
## 8 -0.24850710 0.02968595
## 9 -0.18538528 0.09092028
## 10 -0.18725239 0.07583360
## 11 -0.17400949 0.04668447
## 12 -0.18986613 0.03246979
## 13 -0.15774713 -0.01008217
## 14 -0.13342854 -0.04789767
## 15 -0.11929088 -0.08091858
## 16 -0.11027967 -0.11996861
## 17 -0.04928729 -0.02624667
## 18 0.05258660 -0.00551844
## 19 -0.10220916 -0.15301803
## 20 -0.11805238 0.05155455
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.8979572 -0.02981141 0.22937183
## 2 -0.9238416 -0.02200388 0.21716331
## 3 -0.8790757 -0.10098454 0.21204923
## 4 -0.9636481 -0.01863653 0.20784810
## 5 -0.8376476 -0.06988959 0.19667254
## 6 -0.9548616 -0.02822308 0.17715161
## 7 -0.9213182 -0.03185417 0.16119033
## 8 -0.7954583 -0.01897317 0.15413495
## 9 -0.8858472 -0.04028454 0.15591811
## 10 -0.8552462 -0.04462247 0.13722150
## 11 -0.8152510 -0.02610660 0.11178624
## 12 -0.8060020 0.03935814 0.10145368
## 13 -0.7768847 -0.02802684 0.08157248
## 14 -0.7534444 -0.02355878 0.07508420
## 15 -0.7363116 -0.03009705 0.06653922
## 16 -0.7114120 -0.02722074 0.05308246
## 17 -0.8061073 0.06533123 0.10579168
## 18 -1.0378013 0.01477629 0.22235651
## 19 -0.6967970 -0.02332284 0.04543569
## 20 -0.8092120 0.11843684 0.03785004
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.1941208 9 -245.9326 510.6761 0.00000000 0.2452700598
## 2 -0.2213759 9 -245.9783 510.7673 0.09123205 0.2343331600
## 3 -0.1894311 9 -246.3421 511.4950 0.81887275 0.1628653055
## 4 -0.2079877 9 -246.5974 512.0056 1.32946159 0.1261698596
## 5 -0.1718898 9 -247.2201 513.2510 2.57489060 0.0676883841
## 6 -0.2047941 9 -247.5040 513.8189 3.14276088 0.0509568628
## 7 -0.2197413 9 -248.0947 515.0003 4.32417953 0.0282267503
## 8 -0.1994415 9 -248.4946 515.8000 5.12395786 0.0189230539
## 9 -0.2563423 9 -248.5318 515.8745 5.19837377 0.0182319036
## 10 -0.2519521 9 -249.1444 517.0996 6.42347195 0.0098811007
## 11 -0.2755337 9 -249.4667 517.7442 7.06813258 0.0071584509
## 12 -0.2528909 9 -249.5529 517.9167 7.24058974 0.0065670520
## 13 -0.2591065 9 -249.7164 518.2436 7.56753821 0.0055766642
## 14 -0.2632859 9 -249.9975 518.8059 8.12978781 0.0042100140
## 15 -0.2413098 9 -250.3088 519.4284 8.75231613 0.0030839210
## 16 -0.2210611 9 -250.4485 519.7077 9.03164485 0.0026819321
## 17 -0.3217369 9 -250.4932 519.7971 9.12105046 0.0025646824
## 18 -0.0940118 9 -250.5437 519.8982 9.22212852 0.0024382867
## 19 -0.1976244 9 -250.5647 519.9403 9.26416148 0.0023875772
## 20 -0.1413821 9 -251.6771 522.1650 11.48891619 0.0007849791
## Dataset
## 1 osm_anthro_df_2021_2022
## 2 osm_anthro_df_2021_2022
## 3 osm_anthro_df_2021_2022
## 4 osm_anthro_df_2021_2022
## 5 osm_anthro_df_2021_2022
## 6 osm_anthro_df_2021_2022
## 7 osm_anthro_df_2021_2022
## 8 osm_anthro_df_2021_2022
## 9 osm_anthro_df_2021_2022
## 10 osm_anthro_df_2021_2022
## 11 osm_anthro_df_2021_2022
## 12 osm_anthro_df_2021_2022
## 13 osm_anthro_df_2021_2022
## 14 osm_anthro_df_2021_2022
## 15 osm_anthro_df_2021_2022
## 16 osm_anthro_df_2021_2022
## 17 osm_anthro_df_2021_2022
## 18 osm_anthro_df_2021_2022
## 19 osm_anthro_df_2021_2022
## 20 osm_anthro_df_2021_2022summary(wolf_anthro_mods$`2000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(grey_wolf, absent_grey_wolf) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 509.9 540.9 -245.9 491.9 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.1494 0.3865
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.22060 0.20918 -15.396 < 2e-16 ***
## scale(harvest) 0.10337 0.11432 0.904 0.36588
## scale(seismic_lines) -0.02981 0.11606 -0.257 0.79729
## scale(seismic_lines_3D) -0.89796 0.29493 -3.045 0.00233 **
## scale(trails) 0.22937 0.10300 2.227 0.02595 *
## scale(wells) -0.19412 0.19809 -0.980 0.32711
## scale(osm_industrial) -0.32843 0.18340 -1.791 0.07333 .
## scale(pipeline_transmission_lines) 0.11514 0.13458 0.856 0.39225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Lynx
lynx_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(lynx, absent_lynx) ~
# HFI that aren't correlated
scale(harvest) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
lynx_anthro_model.sel <- model.sel(lynx_anthro_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
lynx_anthro_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 250 meter buffer -1.894836 + -0.29467069
## 2 500 meter buffer -1.873101 + -0.21569198
## 3 4250 meter buffer -1.878132 + 0.17708328
## 4 5000 meter buffer -1.875329 + 0.13109992
## 5 4750 meter buffer -1.875487 + 0.14567041
## 6 3750 meter buffer -1.877733 + 0.16606727
## 7 4500 meter buffer -1.876813 + 0.17013676
## 8 3500 meter buffer -1.876248 + 0.15982760
## 9 4000 meter buffer -1.877722 + 0.17156337
## 10 1000 meter buffer -1.866105 + -0.11440623
## 11 3250 meter buffer -1.875161 + 0.15400246
## 12 750 meter buffer -1.866773 + -0.15052077
## 13 3000 meter buffer -1.873577 + 0.14246691
## 14 1250 meter buffer -1.865800 + -0.10474335
## 15 2750 meter buffer -1.870552 + 0.12220359
## 16 1500 meter buffer -1.863552 + -0.07129125
## 17 2000 meter buffer -1.865072 + 0.03121712
## 18 2500 meter buffer -1.867720 + 0.09170598
## 19 1750 meter buffer -1.863455 + -0.01426631
## 20 2250 meter buffer -1.866236 + 0.06938032
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 0.09425034 0.006616863
## 2 0.10293881 -0.019673189
## 3 0.05193274 -0.035048833
## 4 0.08080414 -0.058032732
## 5 0.07244795 -0.050452241
## 6 0.04198486 -0.022294739
## 7 0.06234069 -0.050635027
## 8 0.04247588 -0.012373846
## 9 0.04479930 -0.037003527
## 10 0.07094306 0.019786653
## 11 0.04846624 -0.012902275
## 12 0.09380222 0.002609593
## 13 0.04266891 -0.012630281
## 14 0.05541717 0.022198793
## 15 0.04221334 -0.006783697
## 16 0.04894335 0.026627004
## 17 0.04712056 0.028358338
## 18 0.04379083 -0.007383012
## 19 0.04890621 0.028220546
## 20 0.04817181 0.000730868
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 0.07457654 -0.165728236 0.06159282
## 2 0.06937945 -0.081084614 -0.01431302
## 3 0.07412847 -0.190623564 0.09286601
## 4 0.10098593 -0.175148432 0.08851419
## 5 0.09101677 -0.172064735 0.09413081
## 6 0.06482959 -0.195478981 0.09471726
## 7 0.08297493 -0.178906873 0.09220457
## 8 0.06458772 -0.181911915 0.09696013
## 9 0.06961254 -0.190886604 0.09032131
## 10 0.07077167 -0.006769057 0.09299693
## 11 0.07139784 -0.166652239 0.08938725
## 12 0.06941278 -0.037171319 0.03048031
## 13 0.07540087 -0.145048537 0.08869826
## 14 0.07156556 0.039075752 0.10145198
## 15 0.07739493 -0.123695521 0.07960387
## 16 0.07478875 0.036136764 0.10924981
## 17 0.08069574 -0.001349671 0.10336588
## 18 0.07900523 -0.097932646 0.08088922
## 19 0.07889843 0.016555868 0.10430295
## 20 0.08147357 -0.063072555 0.08930522
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.2236997 9 -437.7189 894.2487 0.00000 9.959526e-01
## 2 -0.2004746 9 -444.1866 907.1839 12.93525 1.546624e-03
## 3 -0.1309606 9 -445.7671 910.3449 16.09628 3.184023e-04
## 4 -0.1158957 9 -445.8725 910.5558 16.30715 2.865400e-04
## 5 -0.1196413 9 -445.9482 910.7072 16.45849 2.656579e-04
## 6 -0.1410049 9 -445.9694 910.7496 16.50088 2.600861e-04
## 7 -0.1130591 9 -446.0114 910.8337 16.58499 2.493750e-04
## 8 -0.1499100 9 -446.1870 911.1848 16.93617 2.092160e-04
## 9 -0.1231175 9 -446.2240 911.2587 17.01007 2.016268e-04
## 10 -0.2121816 9 -446.3792 911.5692 17.32049 1.726399e-04
## 11 -0.1602172 9 -446.5210 911.8529 17.60421 1.498075e-04
## 12 -0.1859588 9 -446.9218 912.6544 18.40568 1.003449e-04
## 13 -0.1684109 9 -447.0492 912.9092 18.66056 8.833821e-05
## 14 -0.2184514 9 -447.1562 913.1231 18.87448 7.937745e-05
## 15 -0.1788555 9 -447.9887 914.7882 20.53956 3.452463e-05
## 16 -0.2021069 9 -448.3550 915.5208 21.27218 2.393555e-05
## 17 -0.2143190 9 -448.7222 916.2552 22.00651 1.658006e-05
## 18 -0.1774811 9 -448.7574 916.3255 22.07686 1.600697e-05
## 19 -0.2096346 9 -448.8379 916.4866 22.23789 1.476872e-05
## 20 -0.1881892 9 -448.9228 916.6565 22.40780 1.356586e-05
## Dataset
## 1 osm_anthro_df_2021_2022
## 2 osm_anthro_df_2021_2022
## 3 osm_anthro_df_2021_2022
## 4 osm_anthro_df_2021_2022
## 5 osm_anthro_df_2021_2022
## 6 osm_anthro_df_2021_2022
## 7 osm_anthro_df_2021_2022
## 8 osm_anthro_df_2021_2022
## 9 osm_anthro_df_2021_2022
## 10 osm_anthro_df_2021_2022
## 11 osm_anthro_df_2021_2022
## 12 osm_anthro_df_2021_2022
## 13 osm_anthro_df_2021_2022
## 14 osm_anthro_df_2021_2022
## 15 osm_anthro_df_2021_2022
## 16 osm_anthro_df_2021_2022
## 17 osm_anthro_df_2021_2022
## 18 osm_anthro_df_2021_2022
## 19 osm_anthro_df_2021_2022
## 20 osm_anthro_df_2021_2022summary(lynx_anthro_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(lynx, absent_lynx) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 893.4 924.5 -437.7 875.4 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.1606 0.4007
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.894836 0.174410 -10.864 < 2e-16 ***
## scale(harvest) -0.294671 0.090226 -3.266 0.00109 **
## scale(seismic_lines) -0.165728 0.071320 -2.324 0.02014 *
## scale(seismic_lines_3D) 0.074577 0.053207 1.402 0.16102
## scale(trails) 0.061593 0.052606 1.171 0.24166
## scale(wells) -0.223700 0.073888 -3.028 0.00247 **
## scale(osm_industrial) 0.094250 0.051713 1.823 0.06837 .
## scale(pipeline_transmission_lines) 0.006617 0.059660 0.111 0.91169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Moose
moose_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(moose, absent_moose) ~
# HFI that aren't correlated
scale(harvest) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
moose_anthro_model.sel <- model.sel(moose_anthro_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
moose_anthro_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 750 meter buffer -1.834851 + 0.0183680775
## 2 1000 meter buffer -1.830798 + -0.0007321436
## 3 500 meter buffer -1.826986 + 0.0069889217
## 4 1250 meter buffer -1.821919 + 0.0138909705
## 5 1500 meter buffer -1.821835 + 0.0004848259
## 6 2500 meter buffer -1.818893 + -0.0374481328
## 7 3000 meter buffer -1.818311 + -0.0411993488
## 8 1750 meter buffer -1.819386 + -0.0147424390
## 9 2750 meter buffer -1.817997 + -0.0409496051
## 10 3250 meter buffer -1.816784 + -0.0479719094
## 11 3500 meter buffer -1.815764 + -0.0602364139
## 12 3750 meter buffer -1.814890 + -0.0717997559
## 13 2000 meter buffer -1.817848 + -0.0282153014
## 14 2250 meter buffer -1.817706 + -0.0252919481
## 15 4000 meter buffer -1.813877 + -0.0782905080
## 16 250 meter buffer -1.813660 + -0.0025584285
## 17 4250 meter buffer -1.811849 + -0.0881673185
## 18 4500 meter buffer -1.810556 + -0.0990084223
## 19 5000 meter buffer -1.810027 + -0.1123890697
## 20 4750 meter buffer -1.809936 + -0.1049721696
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 -0.3433885 0.0966437760
## 2 -0.3138647 0.0793705338
## 3 -0.2876972 0.0700746093
## 4 -0.2539251 0.0877363064
## 5 -0.2434553 0.0886337280
## 6 -0.2052588 0.1786350775
## 7 -0.2303478 0.1952696928
## 8 -0.1902955 0.1020286967
## 9 -0.2232597 0.1921100396
## 10 -0.2406866 0.1915315588
## 11 -0.2485917 0.1884764137
## 12 -0.2566540 0.1806878808
## 13 -0.1659176 0.1176421042
## 14 -0.1769857 0.1449500609
## 15 -0.2502905 0.1638160723
## 16 -0.1620849 -0.0008116912
## 17 -0.2331126 0.1441355832
## 18 -0.2127881 0.1356954426
## 19 -0.1832783 0.1556111815
## 20 -0.1909256 0.1413337033
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 0.087406667 0.015413734 0.05467494
## 2 0.068487407 -0.050993190 0.07085907
## 3 0.100326796 0.069436351 0.06798361
## 4 0.054913317 -0.038167047 0.08574822
## 5 0.045153145 -0.065373593 0.09490797
## 6 -0.024933718 -0.012025228 0.09334724
## 7 -0.032250519 -0.030413547 0.09251953
## 8 0.027391544 -0.075249170 0.07995651
## 9 -0.032164018 -0.014056939 0.09192659
## 10 -0.033865855 -0.030785970 0.09264045
## 11 -0.035857910 -0.029600929 0.09262267
## 12 -0.037737671 -0.026343553 0.09552988
## 13 0.008666257 -0.042813700 0.07710227
## 14 -0.008776108 -0.022815706 0.08022005
## 15 -0.037312659 -0.026489429 0.09752037
## 16 0.111910277 0.090396184 0.09089820
## 17 -0.032047910 -0.013893253 0.10077367
## 18 -0.029145450 0.002179302 0.10271458
## 19 -0.033124806 0.019215524 0.10460411
## 20 -0.028302305 0.014932788 0.10208187
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.15573120 9 -433.5507 885.9122 0.0000000 0.5257873895
## 2 -0.20550426 9 -434.0008 886.8123 0.9001188 0.3352369244
## 3 -0.09245836 9 -435.7866 890.3839 4.4717229 0.0562066712
## 4 -0.18125650 9 -437.3077 893.4263 7.5140624 0.0122786961
## 5 -0.20159559 9 -437.3623 893.5354 7.6231891 0.0116266791
## 6 -0.25697587 9 -437.7120 894.2348 8.3226226 0.0081955035
## 7 -0.22388644 9 -437.7451 894.3010 8.3887584 0.0079289270
## 8 -0.25642441 9 -437.8704 894.5516 8.6394094 0.0069949765
## 9 -0.22684062 9 -437.8975 894.6058 8.6936392 0.0068078568
## 10 -0.19943281 9 -438.0525 894.9158 9.0035676 0.0058305603
## 11 -0.18518977 9 -438.2267 895.2642 9.3520104 0.0048983171
## 12 -0.15718134 9 -438.5155 895.8419 9.9296893 0.0036694884
## 13 -0.27734814 9 -438.5776 895.9659 10.0537201 0.0034488362
## 14 -0.26706105 9 -438.5926 895.9961 10.0838984 0.0033971869
## 15 -0.14629008 9 -438.8747 896.5603 10.6480836 0.0025621715
## 16 -0.03078054 9 -439.1295 897.0699 11.1576591 0.0019858904
## 17 -0.14194295 9 -439.6408 898.0924 12.1801654 0.0011910250
## 18 -0.14892773 9 -440.1562 899.1232 13.2109628 0.0007113544
## 19 -0.18440736 9 -440.2555 899.3218 13.4096253 0.0006440907
## 20 -0.17376918 9 -440.3307 899.4721 13.5599468 0.0005974549
## Dataset
## 1 osm_anthro_df_2021_2022
## 2 osm_anthro_df_2021_2022
## 3 osm_anthro_df_2021_2022
## 4 osm_anthro_df_2021_2022
## 5 osm_anthro_df_2021_2022
## 6 osm_anthro_df_2021_2022
## 7 osm_anthro_df_2021_2022
## 8 osm_anthro_df_2021_2022
## 9 osm_anthro_df_2021_2022
## 10 osm_anthro_df_2021_2022
## 11 osm_anthro_df_2021_2022
## 12 osm_anthro_df_2021_2022
## 13 osm_anthro_df_2021_2022
## 14 osm_anthro_df_2021_2022
## 15 osm_anthro_df_2021_2022
## 16 osm_anthro_df_2021_2022
## 17 osm_anthro_df_2021_2022
## 18 osm_anthro_df_2021_2022
## 19 osm_anthro_df_2021_2022
## 20 osm_anthro_df_2021_2022summary(moose_anthro_mods$`750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(moose, absent_moose) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 885.1 916.1 -433.6 867.1 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.2396 0.4895
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.83485 0.20929 -8.767 < 2e-16 ***
## scale(harvest) 0.01837 0.06765 0.272 0.785981
## scale(seismic_lines) 0.01541 0.06597 0.234 0.815250
## scale(seismic_lines_3D) 0.08741 0.06029 1.450 0.147104
## scale(trails) 0.05467 0.05245 1.042 0.297238
## scale(wells) -0.15573 0.08944 -1.741 0.081644 .
## scale(osm_industrial) -0.34339 0.08957 -3.834 0.000126 ***
## scale(pipeline_transmission_lines) 0.09664 0.06979 1.385 0.166093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Red fox
fox_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(red_fox, absent_red_fox) ~
# HFI that aren't correlated
scale(harvest) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
fox_anthro_model.sel <- model.sel(fox_anthro_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
fox_anthro_model.sel## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 1750 meter buffer -4.592612 + -0.26286812
## 2 2000 meter buffer -4.532780 + -0.21717820
## 3 1500 meter buffer -4.486297 + -0.37666940
## 4 2250 meter buffer -4.490386 + -0.24493178
## 5 2500 meter buffer -4.508543 + -0.19353507
## 6 4250 meter buffer -4.480454 + -0.13874649
## 7 4500 meter buffer -4.514180 + -0.14027307
## 8 1250 meter buffer -4.370437 + -0.39906687
## 9 4000 meter buffer -4.452845 + -0.15279827
## 10 3750 meter buffer -4.457178 + -0.16131488
## 11 4750 meter buffer -4.511307 + -0.12339492
## 12 2750 meter buffer -4.467354 + -0.14086498
## 13 5000 meter buffer -4.468562 + -0.08202983
## 14 3000 meter buffer -4.452072 + -0.12976118
## 15 500 meter buffer -4.374015 + -0.18812253
## 16 3250 meter buffer -4.408018 + -0.14975688
## 17 3500 meter buffer -4.427207 + -0.17301465
## 18 1000 meter buffer -4.310002 + -0.38185222
## 19 750 meter buffer -4.324336 + -0.37675609
## 20 250 meter buffer -4.307371 + -0.15799042
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 0.44701079 0.324038108
## 2 0.39515293 0.345598267
## 3 0.36805304 0.217907576
## 4 0.36720262 0.403817550
## 5 0.39306084 0.466149400
## 6 0.28236079 0.714919142
## 7 0.42598222 0.753243079
## 8 0.31616394 0.059675277
## 9 0.11613881 0.719388279
## 10 0.07542929 0.758485305
## 11 0.44767922 0.766823832
## 12 0.35289758 0.502462046
## 13 0.42312798 0.655295194
## 14 0.30135658 0.600864352
## 15 0.38674306 0.053999230
## 16 0.24888709 0.562203488
## 17 0.21850584 0.702367548
## 18 0.19832465 0.008758208
## 19 0.30654177 0.130319615
## 20 0.24807610 -0.033861558
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.22085658 -0.4868721 -0.8834670
## 2 -0.29498741 -0.4768147 -0.7528324
## 3 -0.12128691 -0.4365305 -0.7755132
## 4 -0.33791853 -0.3919251 -0.6415524
## 5 -0.36657708 -0.4114253 -0.6323124
## 6 -0.69654461 -0.2709903 -0.3811354
## 7 -0.62738237 -0.3166534 -0.4311867
## 8 -0.13259309 -0.3583284 -0.6933834
## 9 -0.67057909 -0.2413443 -0.2684029
## 10 -0.63565483 -0.2503769 -0.1879981
## 11 -0.65749766 -0.3208702 -0.3783411
## 12 -0.41890581 -0.3420415 -0.4959890
## 13 -0.71136367 -0.2512876 -0.3243299
## 14 -0.42734023 -0.2604621 -0.3654257
## 15 -0.01503751 -0.2116350 -0.6258047
## 16 -0.47190799 -0.1987774 -0.2426482
## 17 -0.50117583 -0.2699965 -0.1972197
## 18 -0.09753932 -0.3227392 -0.6295377
## 19 -0.07750341 -0.1868773 -0.6375869
## 20 0.12026800 -0.1493282 -0.8489739
## cond(scale(wells)) df logLik AICc delta weight
## 1 -0.08828480 9 -173.6990 366.2088 0.000000 7.325857e-01
## 2 0.01458861 9 -175.2407 369.2923 3.083426 1.567838e-01
## 3 -0.03182992 9 -176.2325 371.2759 5.067047 5.815179e-02
## 4 0.04205289 9 -177.8491 374.5089 8.300087 1.154828e-02
## 5 0.01523485 9 -178.3298 375.4705 9.261660 7.140268e-03
## 6 0.21501072 9 -178.6243 376.0595 9.850678 5.318765e-03
## 7 0.09272988 9 -178.7863 376.3835 10.174668 4.523320e-03
## 8 0.19525576 9 -178.7877 376.3862 10.177371 4.517211e-03
## 9 0.27955167 9 -178.8836 376.5781 10.369233 4.104008e-03
## 10 0.25873331 9 -178.9330 376.6768 10.468018 3.906224e-03
## 11 0.08300829 9 -179.1461 377.1030 10.894142 3.156644e-03
## 12 0.11544595 9 -179.2526 377.3159 11.107095 2.837811e-03
## 13 0.21082390 9 -179.9893 378.7895 12.580679 1.358309e-03
## 14 0.16092921 9 -180.0568 378.9244 12.715571 1.269718e-03
## 15 0.47561059 9 -180.1740 379.1589 12.950056 1.129249e-03
## 16 0.25467588 9 -180.5557 379.9223 13.713472 7.709314e-04
## 17 0.14450934 9 -180.5757 379.9621 13.753312 7.557265e-04
## 18 0.25200724 9 -182.7290 384.2688 18.059938 8.773903e-05
## 19 0.29356934 9 -183.2344 385.2795 19.070684 5.293123e-05
## 20 0.13914895 9 -186.7350 392.2808 26.071959 1.597366e-06
## Dataset
## 1 osm_anthro_df_2021_2022
## 2 osm_anthro_df_2021_2022
## 3 osm_anthro_df_2021_2022
## 4 osm_anthro_df_2021_2022
## 5 osm_anthro_df_2021_2022
## 6 osm_anthro_df_2021_2022
## 7 osm_anthro_df_2021_2022
## 8 osm_anthro_df_2021_2022
## 9 osm_anthro_df_2021_2022
## 10 osm_anthro_df_2021_2022
## 11 osm_anthro_df_2021_2022
## 12 osm_anthro_df_2021_2022
## 13 osm_anthro_df_2021_2022
## 14 osm_anthro_df_2021_2022
## 15 osm_anthro_df_2021_2022
## 16 osm_anthro_df_2021_2022
## 17 osm_anthro_df_2021_2022
## 18 osm_anthro_df_2021_2022
## 19 osm_anthro_df_2021_2022
## 20 osm_anthro_df_2021_2022summary(fox_anthro_mods$`1750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(red_fox, absent_red_fox) ~ scale(harvest) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(wells) +
## scale(osm_industrial) + scale(pipeline_transmission_lines) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 365.4 396.4 -173.7 347.4 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 2.963 1.721
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.59261 0.76036 -6.040 1.54e-09 ***
## scale(harvest) -0.26287 0.24399 -1.077 0.281304
## scale(seismic_lines) -0.48687 0.15878 -3.066 0.002167 **
## scale(seismic_lines_3D) -0.22086 0.29037 -0.761 0.446900
## scale(trails) -0.88347 0.25707 -3.437 0.000589 ***
## scale(wells) -0.08828 0.24232 -0.364 0.715610
## scale(osm_industrial) 0.44701 0.17367 2.574 0.010054 *
## scale(pipeline_transmission_lines) 0.32404 0.23535 1.377 0.168557
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1White-tailed deer
deer_anthro_mods <- osm_anthro_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~
# HFI that aren't correlated
scale(harvest) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(wells) +
scale(osm_industrial) +
scale(pipeline_transmission_lines) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
deer_anthro_model.sel <- model.sel(deer_anthro_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
deer_anthro_model.sel ## Model cond((Int)) disp((Int)) cond(scale(harvest))
## 1 1500 meter buffer -0.1949688 + -0.08753506
## 2 1250 meter buffer -0.1941255 + -0.08802588
## 3 1750 meter buffer -0.1896187 + -0.10391011
## 4 4000 meter buffer -0.1970629 + -0.23130893
## 5 3750 meter buffer -0.1931356 + -0.20818608
## 6 2000 meter buffer -0.1887216 + -0.11206117
## 7 2250 meter buffer -0.1851221 + -0.12969512
## 8 4250 meter buffer -0.2003849 + -0.24413573
## 9 3500 meter buffer -0.1920635 + -0.18816085
## 10 3250 meter buffer -0.1878558 + -0.16527370
## 11 4500 meter buffer -0.2039623 + -0.25288839
## 12 2500 meter buffer -0.1853276 + -0.13080511
## 13 2750 meter buffer -0.1840471 + -0.13375845
## 14 1000 meter buffer -0.1872159 + -0.11662570
## 15 3000 meter buffer -0.1846154 + -0.15093998
## 16 4750 meter buffer -0.2081727 + -0.27156534
## 17 5000 meter buffer -0.2115667 + -0.29101335
## 18 750 meter buffer -0.1784914 + -0.08545025
## 19 500 meter buffer -0.1836003 + -0.01531539
## 20 250 meter buffer -0.1848293 + 0.02890545
## cond(scale(osm_industrial)) cond(scale(pipeline_transmission_lines))
## 1 0.3266929 0.068918042
## 2 0.3531894 0.071355953
## 3 0.2887577 0.073991666
## 4 0.2006858 0.016824965
## 5 0.1935731 0.046181915
## 6 0.2602571 0.058618826
## 7 0.2433420 0.048601940
## 8 0.2086745 -0.003816363
## 9 0.1937264 0.047904809
## 10 0.1997387 0.055874076
## 11 0.2178106 -0.019732642
## 12 0.2278389 0.048843146
## 13 0.2179251 0.045337615
## 14 0.3464785 0.091748708
## 15 0.2113551 0.050276531
## 16 0.2228619 -0.046201932
## 17 0.2222918 -0.087870812
## 18 0.3466307 0.127212159
## 19 0.3323732 0.136149032
## 20 0.2452461 0.109232041
## cond(scale(seismic_lines_3D)) cond(scale(seismic_lines)) cond(scale(trails))
## 1 -0.4286887 -0.07579262 0.16605451
## 2 -0.4239948 -0.06642569 0.14971158
## 3 -0.4028014 -0.09030177 0.17126342
## 4 -0.3928509 -0.03262868 0.23454814
## 5 -0.4027613 -0.02544128 0.23608550
## 6 -0.3905780 -0.08964066 0.17165898
## 7 -0.3650659 -0.05132720 0.19006195
## 8 -0.3770892 -0.05001198 0.22406437
## 9 -0.3998331 -0.03283989 0.23055478
## 10 -0.3866642 -0.02725456 0.22161964
## 11 -0.3705582 -0.04690424 0.21741979
## 12 -0.3605450 -0.05163172 0.20204683
## 13 -0.3666263 -0.03938735 0.20047128
## 14 -0.3536116 -0.07066707 0.14494716
## 15 -0.3691539 -0.02600635 0.20431238
## 16 -0.3561955 -0.03453529 0.21628748
## 17 -0.3352126 -0.02876257 0.21748385
## 18 -0.2964609 -0.02424617 0.08335996
## 19 -0.2827067 0.02861321 0.09759079
## 20 -0.2470869 -0.04876880 0.18617039
## cond(scale(wells)) df logLik AICc delta weight
## 1 0.3433686 9 -565.1554 1149.122 0.000000 7.322431e-01
## 2 0.3240821 9 -566.4778 1151.766 2.644627 1.951560e-01
## 3 0.3455140 9 -567.7221 1154.255 5.133266 5.623164e-02
## 4 0.4280328 9 -570.4370 1159.685 10.563062 3.723193e-03
## 5 0.4250871 9 -570.4419 1159.695 10.572899 3.704925e-03
## 6 0.3599137 9 -570.6865 1160.184 11.062160 2.900933e-03
## 7 0.3895186 9 -571.2485 1161.308 12.186166 1.653723e-03
## 8 0.4254652 9 -571.5245 1161.860 12.738132 1.254888e-03
## 9 0.4152409 9 -571.7318 1162.274 13.152770 1.019923e-03
## 10 0.4150440 9 -572.5072 1163.825 14.703526 4.697051e-04
## 11 0.4238344 9 -572.5605 1163.932 14.810105 4.453300e-04
## 12 0.3944214 9 -572.8046 1164.420 15.298292 3.488779e-04
## 13 0.4158101 9 -573.1861 1165.183 16.061241 2.382325e-04
## 14 0.2919624 9 -573.2028 1165.216 16.094653 2.342857e-04
## 15 0.4168705 9 -573.3591 1165.529 16.407254 2.003851e-04
## 16 0.4201377 9 -574.0336 1166.878 17.756217 1.020803e-04
## 17 0.4348209 9 -574.3720 1167.555 18.433195 7.276758e-05
## 18 0.2746656 9 -584.3733 1187.557 38.435784 3.299369e-09
## 19 0.2579680 9 -590.4102 1199.631 50.509463 7.882514e-12
## 20 0.0664280 9 -598.5895 1215.990 66.868014 2.210296e-15
## Dataset
## 1 osm_anthro_df_2021_2022
## 2 osm_anthro_df_2021_2022
## 3 osm_anthro_df_2021_2022
## 4 osm_anthro_df_2021_2022
## 5 osm_anthro_df_2021_2022
## 6 osm_anthro_df_2021_2022
## 7 osm_anthro_df_2021_2022
## 8 osm_anthro_df_2021_2022
## 9 osm_anthro_df_2021_2022
## 10 osm_anthro_df_2021_2022
## 11 osm_anthro_df_2021_2022
## 12 osm_anthro_df_2021_2022
## 13 osm_anthro_df_2021_2022
## 14 osm_anthro_df_2021_2022
## 15 osm_anthro_df_2021_2022
## 16 osm_anthro_df_2021_2022
## 17 osm_anthro_df_2021_2022
## 18 osm_anthro_df_2021_2022
## 19 osm_anthro_df_2021_2022
## 20 osm_anthro_df_2021_2022summary(deer_anthro_mods$`1500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~ scale(harvest) +
## scale(seismic_lines) + scale(seismic_lines_3D) + scale(trails) +
## scale(wells) + scale(osm_industrial) + scale(pipeline_transmission_lines) +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 1148.3 1179.3 -565.2 1130.3 223
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 1.779 1.334
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.19497 0.54694 -0.356 0.72149
## scale(harvest) -0.08754 0.06511 -1.344 0.17879
## scale(seismic_lines) -0.07579 0.06319 -1.199 0.23039
## scale(seismic_lines_3D) -0.42869 0.09018 -4.754 2.00e-06 ***
## scale(trails) 0.16605 0.05173 3.210 0.00133 **
## scale(wells) 0.34337 0.07246 4.739 2.15e-06 ***
## scale(osm_industrial) 0.32669 0.05488 5.953 2.63e-09 ***
## scale(pipeline_transmission_lines) 0.06892 0.07549 0.913 0.36126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Save model results
We also want to save the output from the model.sel funciton for each species as one csv file for use plotting later like we did with the global analysis
# provide list of model.sel data frames from global analysis
anthro_model.sel_data <- list(
bear = bear_anthro_model.sel,
caribou = caribou_anthro_model.sel,
coyote = coyote_anthro_model.sel,
fisher = fisher_anthro_model.sel,
wolf = wolf_anthro_model.sel,
lynx = lynx_anthro_model.sel,
moose = moose_anthro_model.sel,
fox = fox_anthro_model.sel,
deer = deer_anthro_model.sel
) %>%
# use purrr to combine data and extract species names to use as a column
map_dfr(~.x %>%
mutate(species = deparse(substitute(.x))),
.id = "species")And save this for use later
write_csv(anthro_model.sel_data,
'data/processed/OSM_glm_anthro_model_sel_data.csv')Landscape
Now we will duplicate this analysis but for data that only includes landscape features.
For now we have to select EITHER lc_forest or lc_shrub as they are highly correlated
Black bear
bear_land_mods <- osm_landscape_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# Landscape classes that aren't correlated
scale(lc_forest) +
scale(lc_grassland) +
scale(lc_developed) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
bear_land_model.sel <- model.sel(bear_land_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
bear_land_model.sel ## Model cond((Int)) disp((Int)) cond(scale(lc_developed))
## 1 250 meter buffer -0.5940062 + -0.22455623
## 2 500 meter buffer -0.5921419 + -0.12596244
## 3 1000 meter buffer -0.5902570 + -0.08992757
## 4 1250 meter buffer -0.5899130 + -0.09248092
## 5 750 meter buffer -0.5913420 + -0.10334653
## 6 1500 meter buffer -0.5892872 + -0.08210297
## 7 2000 meter buffer -0.5885890 + -0.06772306
## 8 2250 meter buffer -0.5881483 + -0.06175796
## 9 2500 meter buffer -0.5882895 + -0.06528588
## 10 1750 meter buffer -0.5888792 + -0.06971220
## 11 2750 meter buffer -0.5889258 + -0.07668298
## 12 3000 meter buffer -0.5889580 + -0.07805944
## 13 3500 meter buffer -0.5892280 + -0.08398031
## 14 3750 meter buffer -0.5894918 + -0.08584491
## 15 3250 meter buffer -0.5890142 + -0.07954595
## 16 4000 meter buffer -0.5898104 + -0.08629769
## 17 4250 meter buffer -0.5900128 + -0.08116356
## 18 4500 meter buffer -0.5900676 + -0.07534982
## 19 5000 meter buffer -0.5896616 + -0.06409369
## 20 4750 meter buffer -0.5896032 + -0.06501601
## cond(scale(lc_forest)) cond(scale(lc_grassland)) df logLik AICc
## 1 -0.166545966 -0.0338886818 5 -456.3951 923.0557
## 2 -0.062020095 0.0189681862 5 -460.9899 932.2453
## 3 0.008256353 -0.0321157828 5 -461.0813 932.4281
## 4 -0.009789065 -0.0352047456 5 -461.1460 932.5576
## 5 -0.005930996 -0.0007372826 5 -461.4152 933.0958
## 6 -0.007234420 -0.0284842932 5 -461.5450 933.3554
## 7 0.020710391 -0.0290792061 5 -461.6240 933.5134
## 8 0.020953730 -0.0354869272 5 -461.6914 933.6482
## 9 0.017800095 -0.0274581274 5 -461.7985 933.8625
## 10 0.008760172 -0.0223054735 5 -461.8327 933.9309
## 11 0.014033317 -0.0054353999 5 -461.8822 934.0299
## 12 0.008553426 -0.0036752402 5 -461.9418 934.1491
## 13 -0.015144122 0.0004516173 5 -462.0006 934.2667
## 14 -0.024249011 0.0057615924 5 -462.0108 934.2870
## 15 -0.004495071 -0.0014421209 5 -462.0464 934.3584
## 16 -0.028752071 0.0147876169 5 -462.0493 934.3642
## 17 -0.034458698 0.0192829301 5 -462.1781 934.6216
## 18 -0.042564861 0.0195784137 5 -462.2521 934.7696
## 19 -0.055883047 0.0020862330 5 -462.2547 934.7750
## 20 -0.048356628 0.0046211298 5 -462.3337 934.9329
## delta weight Dataset
## 1 0.000000 0.914581004 osm_anthro_df_2021_2022
## 2 9.189548 0.009241386 osm_anthro_df_2021_2022
## 3 9.372341 0.008434206 osm_anthro_df_2021_2022
## 4 9.501854 0.007905345 osm_anthro_df_2021_2022
## 5 10.040106 0.006040055 osm_anthro_df_2021_2022
## 6 10.299733 0.005304735 osm_anthro_df_2021_2022
## 7 10.457723 0.004901810 osm_anthro_df_2021_2022
## 8 10.592532 0.004582295 osm_anthro_df_2021_2022
## 9 10.806775 0.004116811 osm_anthro_df_2021_2022
## 10 10.875201 0.003978344 osm_anthro_df_2021_2022
## 11 10.974181 0.003786248 osm_anthro_df_2021_2022
## 12 11.093416 0.003567119 osm_anthro_df_2021_2022
## 13 11.211013 0.003363424 osm_anthro_df_2021_2022
## 14 11.231311 0.003329461 osm_anthro_df_2021_2022
## 15 11.302670 0.003212762 osm_anthro_df_2021_2022
## 16 11.308445 0.003203498 osm_anthro_df_2021_2022
## 17 11.565912 0.002816543 osm_anthro_df_2021_2022
## 18 11.713873 0.002615694 osm_anthro_df_2021_2022
## 19 11.719248 0.002608674 osm_anthro_df_2021_2022
## 20 11.877193 0.002410586 osm_anthro_df_2021_2022summary(bear_land_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(black_bear, absent_black_bear) ~ scale(lc_forest) + scale(lc_grassland) +
## scale(lc_developed) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 922.8 940.0 -456.4 912.8 227
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.1092 0.3304
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.59401 0.14308 -4.151 3.3e-05 ***
## scale(lc_forest) -0.16655 0.06698 -2.487 0.012898 *
## scale(lc_grassland) -0.03389 0.05487 -0.618 0.536799
## scale(lc_developed) -0.22456 0.06436 -3.489 0.000485 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Caribou
caribou_land_mods <- osm_landscape_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(caribou, absent_caribou) ~
# Landscape classes that aren't correlated
scale(lc_forest) +
scale(lc_grassland) +
scale(lc_developed) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
caribou_land_model.sel <- model.sel(caribou_land_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
caribou_land_model.sel ## Model cond((Int)) disp((Int)) cond(scale(lc_developed))
## 1 250 meter buffer -5.039744 + -0.07920529
## 2 1250 meter buffer -5.073363 + -0.47573584
## 3 1500 meter buffer -5.073111 + -0.46442173
## 4 500 meter buffer -5.020600 + 0.02154320
## 5 750 meter buffer -5.022980 + -0.10813319
## 6 1750 meter buffer -5.073948 + -0.52212117
## 7 1000 meter buffer -5.039413 + -0.33123049
## 8 2000 meter buffer -5.036072 + -0.41612035
## 9 4750 meter buffer -5.016494 + 0.02212472
## 10 4500 meter buffer -5.021824 + 0.09100185
## 11 2250 meter buffer -5.017428 + -0.33761804
## 12 5000 meter buffer -5.020685 + 0.09191273
## 13 4250 meter buffer -5.017737 + 0.10874223
## 14 2500 meter buffer -5.007387 + -0.29407088
## 15 2750 meter buffer -5.002110 + -0.26287406
## 16 4000 meter buffer -5.014757 + 0.12757463
## 17 3750 meter buffer -4.995640 + -0.01349521
## 18 3500 meter buffer -4.993128 + -0.05918197
## 19 3000 meter buffer -4.993432 + -0.19813164
## 20 3250 meter buffer -4.988324 + -0.10344989
## cond(scale(lc_forest)) cond(scale(lc_grassland)) df logLik AICc
## 1 0.221768285 -0.15411729 5 -150.5854 311.4363
## 2 0.118284339 -0.12061144 5 -151.1989 312.6634
## 3 0.121616392 -0.14279265 5 -151.2090 312.6835
## 4 0.239995535 -0.12290176 5 -151.3883 313.0422
## 5 0.197592620 -0.15224614 5 -151.5661 313.3977
## 6 0.106181455 -0.10122006 5 -151.6441 313.5536
## 7 0.142141484 -0.09792318 5 -151.9191 314.1037
## 8 0.090467148 -0.08011251 5 -152.5766 315.4186
## 9 -0.050299764 -0.20895635 5 -152.9150 316.0955
## 10 -0.038887723 -0.21200637 5 -152.9523 316.1700
## 11 0.074887644 -0.08964834 5 -152.9810 316.2274
## 12 -0.059831519 -0.19529503 5 -153.0109 316.2872
## 13 -0.035484610 -0.18805158 5 -153.1334 316.5322
## 14 0.057850255 -0.09180166 5 -153.2063 316.6782
## 15 0.043151889 -0.09703247 5 -153.3160 316.8975
## 16 -0.038377535 -0.15636930 5 -153.3353 316.9361
## 17 -0.023504821 -0.13742783 5 -153.4555 317.1764
## 18 -0.001796785 -0.13123674 5 -153.4796 317.2247
## 19 0.032108527 -0.09638687 5 -153.4846 317.2347
## 20 0.015118354 -0.10318028 5 -153.6017 317.4689
## delta weight Dataset
## 1 0.000000 0.22151625 osm_anthro_df_2021_2022
## 2 1.227057 0.11993711 osm_anthro_df_2021_2022
## 3 1.247209 0.11873469 osm_anthro_df_2021_2022
## 4 1.605845 0.09924320 osm_anthro_df_2021_2022
## 5 1.961364 0.08308081 osm_anthro_df_2021_2022
## 6 2.117316 0.07684866 osm_anthro_df_2021_2022
## 7 2.667427 0.05836887 osm_anthro_df_2021_2022
## 8 3.982290 0.03024561 osm_anthro_df_2021_2022
## 9 4.659132 0.02156195 osm_anthro_df_2021_2022
## 10 4.733686 0.02077298 osm_anthro_df_2021_2022
## 11 4.791080 0.02018533 osm_anthro_df_2021_2022
## 12 4.850896 0.01959056 osm_anthro_df_2021_2022
## 13 5.095873 0.01733208 osm_anthro_df_2021_2022
## 14 5.241848 0.01611212 osm_anthro_df_2021_2022
## 15 5.461181 0.01443860 osm_anthro_df_2021_2022
## 16 5.499741 0.01416289 osm_anthro_df_2021_2022
## 17 5.740115 0.01255901 osm_anthro_df_2021_2022
## 18 5.788329 0.01225987 osm_anthro_df_2021_2022
## 19 5.798337 0.01219867 osm_anthro_df_2021_2022
## 20 6.032527 0.01085073 osm_anthro_df_2021_2022summary(caribou_land_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(caribou, absent_caribou) ~ scale(lc_forest) + scale(lc_grassland) +
## scale(lc_developed) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 311.2 328.4 -150.6 301.2 227
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 5.351 2.313
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.03974 1.10127 -4.576 4.73e-06 ***
## scale(lc_forest) 0.22177 0.15873 1.397 0.162
## scale(lc_grassland) -0.15412 0.16528 -0.932 0.351
## scale(lc_developed) -0.07921 0.20030 -0.395 0.693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Coyote
coyote_land_mods <- osm_landscape_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(coyote, absent_coyote) ~
# Landscape classes that aren't correlated
scale(lc_forest) +
scale(lc_grassland) +
scale(lc_developed) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
coyote_land_model.sel <- model.sel(coyote_land_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
coyote_land_model.sel ## Model cond((Int)) disp((Int)) cond(scale(lc_developed))
## 1 3750 meter buffer -1.368761 + 0.4529572
## 2 4500 meter buffer -1.365998 + 0.4741382
## 3 4750 meter buffer -1.364961 + 0.4811141
## 4 5000 meter buffer -1.363739 + 0.4857252
## 5 3500 meter buffer -1.368307 + 0.4483529
## 6 4250 meter buffer -1.367207 + 0.4594487
## 7 4000 meter buffer -1.368569 + 0.4494386
## 8 3250 meter buffer -1.368265 + 0.4331079
## 9 3000 meter buffer -1.367635 + 0.4248031
## 10 1250 meter buffer -1.366635 + 0.4172910
## 11 1750 meter buffer -1.363617 + 0.4389814
## 12 2750 meter buffer -1.366238 + 0.4196682
## 13 2000 meter buffer -1.363295 + 0.4372304
## 14 1500 meter buffer -1.365343 + 0.4235781
## 15 2250 meter buffer -1.364285 + 0.4356357
## 16 2500 meter buffer -1.365244 + 0.4240073
## 17 1000 meter buffer -1.368914 + 0.3838178
## 18 750 meter buffer -1.370036 + 0.3340027
## 19 500 meter buffer -1.369698 + 0.2110103
## 20 250 meter buffer -1.373489 + 0.1435200
## cond(scale(lc_forest)) cond(scale(lc_grassland)) df logLik AICc
## 1 -0.2504571 -0.075337957 5 -486.1135 982.4925
## 2 -0.2354329 -0.092235973 5 -486.1745 982.6145
## 3 -0.2305974 -0.090665973 5 -486.1813 982.6280
## 4 -0.2254750 -0.091480668 5 -486.4594 983.1844
## 5 -0.2468286 -0.068584943 5 -486.7078 983.6810
## 6 -0.2404484 -0.083466339 5 -486.9957 984.2569
## 7 -0.2481005 -0.074203486 5 -487.0113 984.2881
## 8 -0.2468445 -0.052268277 5 -488.0951 986.4557
## 9 -0.2440525 -0.050349030 5 -489.1893 988.6441
## 10 -0.1826078 -0.017412912 5 -490.4368 991.1391
## 11 -0.1670040 -0.050528762 5 -490.5229 991.3113
## 12 -0.2305268 -0.055643903 5 -490.9381 992.1416
## 13 -0.1757053 -0.072877408 5 -491.2952 992.8558
## 14 -0.1715850 -0.028573541 5 -491.3307 992.9268
## 15 -0.1913409 -0.083067018 5 -491.4137 993.0930
## 16 -0.2120977 -0.070989096 5 -491.6408 993.5471
## 17 -0.1960226 0.009004420 5 -491.8013 993.8681
## 18 -0.1861901 0.048059099 5 -497.9676 1006.2006
## 19 -0.1916414 0.039140690 5 -513.3686 1037.0028
## 20 -0.1977173 0.006203179 5 -519.6501 1049.5657
## delta weight Dataset
## 1 0.0000000 1.924163e-01 osm_anthro_df_2021_2022
## 2 0.1219884 1.810308e-01 osm_anthro_df_2021_2022
## 3 0.1354810 1.798136e-01 osm_anthro_df_2021_2022
## 4 0.6918619 1.361463e-01 osm_anthro_df_2021_2022
## 5 1.1885301 1.062076e-01 osm_anthro_df_2021_2022
## 6 1.7643536 7.963745e-02 osm_anthro_df_2021_2022
## 7 1.7955616 7.840443e-02 osm_anthro_df_2021_2022
## 8 3.9632395 2.652377e-02 osm_anthro_df_2021_2022
## 9 6.1516030 8.880517e-03 osm_anthro_df_2021_2022
## 10 8.6465481 2.550749e-03 osm_anthro_df_2021_2022
## 11 8.8188190 2.340236e-03 osm_anthro_df_2021_2022
## 12 9.6490889 1.545143e-03 osm_anthro_df_2021_2022
## 13 10.3632991 1.081135e-03 osm_anthro_df_2021_2022
## 14 10.4343382 1.043408e-03 osm_anthro_df_2021_2022
## 15 10.6004605 9.602429e-04 osm_anthro_df_2021_2022
## 16 11.0545476 7.652042e-04 osm_anthro_df_2021_2022
## 17 11.3755846 6.517260e-04 osm_anthro_df_2021_2022
## 18 23.7081158 1.368012e-06 osm_anthro_df_2021_2022
## 19 54.5102766 2.802113e-13 osm_anthro_df_2021_2022
## 20 67.0732387 5.241711e-16 osm_anthro_df_2021_2022summary(coyote_land_mods$`3750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(coyote, absent_coyote) ~ scale(lc_forest) + scale(lc_grassland) +
## scale(lc_developed) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 982.2 999.5 -486.1 972.2 227
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.1374 0.3707
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.36876 0.16060 -8.523 < 2e-16 ***
## scale(lc_forest) -0.25046 0.05751 -4.355 1.33e-05 ***
## scale(lc_grassland) -0.07534 0.06625 -1.137 0.255
## scale(lc_developed) 0.45296 0.06442 7.032 2.04e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fisher
fisher_land_mods <- osm_landscape_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(fisher, absent_fisher) ~
# Landscape classes that aren't correlated
scale(lc_forest) +
scale(lc_grassland) +
scale(lc_developed) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
fisher_land_model.sel <- model.sel(fisher_land_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
fisher_land_model.sel ## Model cond((Int)) disp((Int)) cond(scale(lc_developed))
## 1 2500 meter buffer -2.968379 + 0.4462479
## 2 3250 meter buffer -2.964730 + 0.4788544
## 3 2750 meter buffer -2.967005 + 0.4573525
## 4 3000 meter buffer -2.965289 + 0.4730753
## 5 2250 meter buffer -2.968784 + 0.4207583
## 6 3500 meter buffer -2.959554 + 0.4584811
## 7 2000 meter buffer -2.970962 + 0.4006872
## 8 3750 meter buffer -2.951848 + 0.4343198
## 9 4250 meter buffer -2.949502 + 0.4292259
## 10 4000 meter buffer -2.949604 + 0.4244698
## 11 4500 meter buffer -2.946984 + 0.4115209
## 12 4750 meter buffer -2.945720 + 0.3971584
## 13 1750 meter buffer -2.963963 + 0.3641566
## 14 5000 meter buffer -2.943661 + 0.3765220
## 15 1250 meter buffer -2.968150 + 0.3575414
## 16 1000 meter buffer -2.980891 + 0.3499200
## 17 750 meter buffer -2.988100 + 0.3899083
## 18 500 meter buffer -2.988047 + 0.4346195
## 19 1500 meter buffer -2.959700 + 0.3511111
## 20 250 meter buffer -2.942422 + 0.2841211
## cond(scale(lc_forest)) cond(scale(lc_grassland)) df logLik AICc
## 1 0.4626473 -0.18883198 5 -278.7345 567.7345
## 2 0.4208578 -0.26410640 5 -278.8745 568.0145
## 3 0.4539606 -0.20704398 5 -279.0308 568.3271
## 4 0.4321033 -0.24437884 5 -279.0621 568.3897
## 5 0.4861173 -0.11844450 5 -279.7041 569.6737
## 6 0.4082150 -0.24854140 5 -279.7154 569.6963
## 7 0.5042086 -0.07794105 5 -280.3559 570.9773
## 8 0.3841964 -0.22070440 5 -280.8247 571.9149
## 9 0.3683729 -0.24125958 5 -280.8461 571.9576
## 10 0.3701224 -0.22623898 5 -281.0735 572.4125
## 11 0.3672068 -0.21980574 5 -281.5093 573.2840
## 12 0.3680595 -0.19849247 5 -281.8277 573.9209
## 13 0.4814612 -0.04320923 5 -282.3687 575.0029
## 14 0.3686674 -0.16747161 5 -282.4228 575.1112
## 15 0.5177533 0.02741755 5 -282.5875 575.4406
## 16 0.5883085 0.07927383 5 -282.6993 575.6640
## 17 0.6379614 0.15948496 5 -282.7427 575.7509
## 18 0.6500935 0.24801315 5 -282.9647 576.1949
## 19 0.4675019 -0.00922415 5 -283.0227 576.3109
## 20 0.4274624 0.22361368 5 -288.8675 588.0004
## delta weight Dataset
## 1 0.0000000 2.052411e-01 osm_anthro_df_2021_2022
## 2 0.2799928 1.784286e-01 osm_anthro_df_2021_2022
## 3 0.5926252 1.526080e-01 osm_anthro_df_2021_2022
## 4 0.6552383 1.479044e-01 osm_anthro_df_2021_2022
## 5 1.9392371 7.783309e-02 osm_anthro_df_2021_2022
## 6 1.9618019 7.695988e-02 osm_anthro_df_2021_2022
## 7 3.2428047 4.056002e-02 osm_anthro_df_2021_2022
## 8 4.1804337 2.538018e-02 osm_anthro_df_2021_2022
## 9 4.2231469 2.484389e-02 osm_anthro_df_2021_2022
## 10 4.6780181 1.978999e-02 osm_anthro_df_2021_2022
## 11 5.5495272 1.279970e-02 osm_anthro_df_2021_2022
## 12 6.1863565 9.309235e-03 osm_anthro_df_2021_2022
## 13 7.2684161 5.419357e-03 osm_anthro_df_2021_2022
## 14 7.3766708 5.133819e-03 osm_anthro_df_2021_2022
## 15 7.7060792 4.354220e-03 osm_anthro_df_2021_2022
## 16 7.9295393 3.893917e-03 osm_anthro_df_2021_2022
## 17 8.0163988 3.728425e-03 osm_anthro_df_2021_2022
## 18 8.4603649 2.986203e-03 osm_anthro_df_2021_2022
## 19 8.5764341 2.817833e-03 osm_anthro_df_2021_2022
## 20 20.2659042 8.157913e-06 osm_anthro_df_2021_2022summary(fisher_land_mods$`2500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(fisher, absent_fisher) ~ scale(lc_forest) + scale(lc_grassland) +
## scale(lc_developed) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 567.5 584.7 -278.7 557.5 227
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.5454 0.7385
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.96838 0.32100 -9.247 < 2e-16 ***
## scale(lc_forest) 0.46265 0.14544 3.181 0.00147 **
## scale(lc_grassland) -0.18883 0.12061 -1.566 0.11745
## scale(lc_developed) 0.44625 0.09245 4.827 1.38e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Grey wolf
wolf_land_mods <- osm_landscape_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(grey_wolf, absent_grey_wolf) ~
# Landscape classes that aren't correlated
scale(lc_forest) +
scale(lc_grassland) +
scale(lc_developed) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
wolf_land_model.sel <- model.sel(wolf_land_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
wolf_land_model.sel ## Model cond((Int)) disp((Int)) cond(scale(lc_developed))
## 1 3500 meter buffer -3.046161 + -0.27916703
## 2 3750 meter buffer -3.046275 + -0.30137364
## 3 4000 meter buffer -3.042264 + -0.33186520
## 4 3250 meter buffer -3.034100 + -0.26426890
## 5 4250 meter buffer -3.033446 + -0.33765275
## 6 4500 meter buffer -3.027710 + -0.33980293
## 7 3000 meter buffer -3.021901 + -0.23722348
## 8 4750 meter buffer -3.019626 + -0.30765699
## 9 5000 meter buffer -3.015489 + -0.28889918
## 10 2750 meter buffer -3.011930 + -0.20286706
## 11 2500 meter buffer -2.998351 + -0.14740111
## 12 500 meter buffer -3.000899 + -0.09243200
## 13 2250 meter buffer -2.991719 + -0.10049864
## 14 250 meter buffer -2.990126 + -0.14612407
## 15 2000 meter buffer -2.988606 + -0.05723996
## 16 1750 meter buffer -2.986130 + -0.01932916
## 17 1500 meter buffer -2.984354 + 0.01408029
## 18 1250 meter buffer -2.982215 + -0.02108745
## 19 750 meter buffer -2.984944 + -0.07707025
## 20 1000 meter buffer -2.982631 + -0.03963140
## cond(scale(lc_forest)) cond(scale(lc_grassland)) df logLik AICc
## 1 0.005707166 0.353789595 5 -259.3789 529.0232
## 2 -0.014531404 0.348327601 5 -259.5091 529.2836
## 3 -0.025042871 0.332041018 5 -259.7174 529.7003
## 4 0.022662089 0.320367395 5 -260.1464 530.5584
## 5 -0.041091273 0.298933049 5 -260.3860 531.0375
## 6 -0.057995075 0.270972883 5 -260.8489 531.9632
## 7 0.021704146 0.276410841 5 -261.2570 532.7794
## 8 -0.064113125 0.240307685 5 -261.6511 533.5677
## 9 -0.079158920 0.216040190 5 -262.1049 534.4754
## 10 0.037407907 0.237442870 5 -262.1129 534.4912
## 11 0.055446663 0.172816949 5 -263.3431 536.9516
## 12 -0.137015483 -0.167191129 5 -263.6123 537.4901
## 13 0.088961442 0.133213377 5 -263.9000 538.0655
## 14 -0.090579964 -0.070318797 5 -264.2212 538.7079
## 15 0.105697539 0.108193337 5 -264.2396 538.7447
## 16 0.123668480 0.075176813 5 -264.4673 539.2000
## 17 0.116839723 0.029148982 5 -264.6449 539.5554
## 18 0.071713342 0.013028003 5 -264.7813 539.8281
## 19 -0.018946655 0.002585728 5 -264.8062 539.8779
## 20 0.035650149 0.003798323 5 -264.8428 539.9511
## delta weight Dataset
## 1 0.0000000 0.242663947 osm_anthro_df_2021_2022
## 2 0.2603872 0.213040859 osm_anthro_df_2021_2022
## 3 0.6770892 0.172972561 osm_anthro_df_2021_2022
## 4 1.5351417 0.112629838 osm_anthro_df_2021_2022
## 5 2.0142701 0.088636391 osm_anthro_df_2021_2022
## 6 2.9399753 0.055795315 osm_anthro_df_2021_2022
## 7 3.7562011 0.037098516 osm_anthro_df_2021_2022
## 8 4.5445063 0.025013718 osm_anthro_df_2021_2022
## 9 5.4521307 0.015888764 osm_anthro_df_2021_2022
## 10 5.4679641 0.015763474 osm_anthro_df_2021_2022
## 11 7.9283727 0.004606605 osm_anthro_df_2021_2022
## 12 8.4668785 0.003519216 osm_anthro_df_2021_2022
## 13 9.0422911 0.002639348 osm_anthro_df_2021_2022
## 14 9.6846391 0.001914311 osm_anthro_df_2021_2022
## 15 9.7214771 0.001879375 osm_anthro_df_2021_2022
## 16 10.1767862 0.001496733 osm_anthro_df_2021_2022
## 17 10.5321307 0.001253090 osm_anthro_df_2021_2022
## 18 10.8048964 0.001093331 osm_anthro_df_2021_2022
## 19 10.8546794 0.001066453 osm_anthro_df_2021_2022
## 20 10.9278230 0.001028155 osm_anthro_df_2021_2022summary(wolf_land_mods$`3500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(grey_wolf, absent_grey_wolf) ~ scale(lc_forest) + scale(lc_grassland) +
## scale(lc_developed) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 528.8 546.0 -259.4 518.8 227
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.3526 0.5938
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.046161 0.267243 -11.398 < 2e-16 ***
## scale(lc_forest) 0.005707 0.149326 0.038 0.96951
## scale(lc_grassland) 0.353790 0.117829 3.003 0.00268 **
## scale(lc_developed) -0.279167 0.148037 -1.886 0.05932 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Lynx
lynx_land_mods <- osm_landscape_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(lynx, absent_lynx) ~
# Landscape classes that aren't correlated
scale(lc_forest) +
scale(lc_grassland) +
scale(lc_developed) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
lynx_land_model.sel <- model.sel(lynx_land_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
lynx_land_model.sel ## Model cond((Int)) disp((Int)) cond(scale(lc_developed))
## 1 500 meter buffer -1.859195 + -0.10277645
## 2 750 meter buffer -1.857541 + -0.06674319
## 3 250 meter buffer -1.856118 + -0.12883312
## 4 1000 meter buffer -1.855711 + -0.04117640
## 5 1250 meter buffer -1.854201 + 0.01610288
## 6 1500 meter buffer -1.853651 + 0.04786925
## 7 4750 meter buffer -1.851128 + 0.04169626
## 8 5000 meter buffer -1.850956 + 0.03511441
## 9 3000 meter buffer -1.854755 + 0.01805685
## 10 4500 meter buffer -1.851382 + 0.03533651
## 11 3250 meter buffer -1.854671 + 0.01480914
## 12 1750 meter buffer -1.852403 + 0.05850045
## 13 2750 meter buffer -1.854269 + 0.01892344
## 14 2000 meter buffer -1.852213 + 0.05903098
## 15 3500 meter buffer -1.853872 + 0.02250146
## 16 3750 meter buffer -1.852866 + 0.03749073
## 17 4000 meter buffer -1.852226 + 0.03489236
## 18 4250 meter buffer -1.851847 + 0.02934343
## 19 2250 meter buffer -1.851994 + 0.05816141
## 20 2500 meter buffer -1.852701 + 0.03872636
## cond(scale(lc_forest)) cond(scale(lc_grassland)) df logLik AICc
## 1 -0.0125606132 0.095236806 5 -452.4006 915.0666
## 2 0.0417150035 0.088067909 5 -453.1013 916.4681
## 3 -0.0552563579 0.005895648 5 -453.3180 916.9016
## 4 0.0430111755 0.078393893 5 -453.5708 917.4072
## 5 0.0536610540 0.070893539 5 -453.7861 917.8377
## 6 0.0516615485 0.051569724 5 -453.9317 918.1289
## 7 -0.0215377485 -0.090991719 5 -453.9398 918.1452
## 8 -0.0158485214 -0.092856771 5 -453.9734 918.2123
## 9 -0.0246544317 0.050075665 5 -454.1851 918.6357
## 10 -0.0272417603 -0.065995812 5 -454.2043 918.6740
## 11 -0.0321049253 0.045353702 5 -454.2210 918.7074
## 12 0.0488104989 0.011149529 5 -454.2696 918.8047
## 13 -0.0171477231 0.044975812 5 -454.2949 918.8553
## 14 0.0424713873 0.002747558 5 -454.3382 918.9419
## 15 -0.0371501581 0.023662900 5 -454.3396 918.9447
## 16 -0.0357463085 -0.006762491 5 -454.3770 919.0196
## 17 -0.0346749204 -0.025899560 5 -454.3956 919.0567
## 18 -0.0315343454 -0.035689015 5 -454.4167 919.0989
## 19 0.0232694641 -0.006766036 5 -454.4480 919.1614
## 20 -0.0002094453 0.014422878 5 -454.5261 919.3178
## delta weight Dataset
## 1 0.000000 0.20670727 osm_anthro_df_2021_2022
## 2 1.401436 0.10257410 osm_anthro_df_2021_2022
## 3 1.834957 0.08258474 osm_anthro_df_2021_2022
## 4 2.340569 0.06413686 osm_anthro_df_2021_2022
## 5 2.771080 0.05171581 osm_anthro_df_2021_2022
## 6 3.062316 0.04470768 osm_anthro_df_2021_2022
## 7 3.078553 0.04434621 osm_anthro_df_2021_2022
## 8 3.145664 0.04288282 osm_anthro_df_2021_2022
## 9 3.569069 0.03470102 osm_anthro_df_2021_2022
## 10 3.607410 0.03404212 osm_anthro_df_2021_2022
## 11 3.640773 0.03347897 osm_anthro_df_2021_2022
## 12 3.738109 0.03188862 osm_anthro_df_2021_2022
## 13 3.788661 0.03109270 osm_anthro_df_2021_2022
## 14 3.875328 0.02977412 osm_anthro_df_2021_2022
## 15 3.878118 0.02973261 osm_anthro_df_2021_2022
## 16 3.952939 0.02864086 osm_anthro_df_2021_2022
## 17 3.990121 0.02811330 osm_anthro_df_2021_2022
## 18 4.032276 0.02752695 osm_anthro_df_2021_2022
## 19 4.094789 0.02667987 osm_anthro_df_2021_2022
## 20 4.251159 0.02467335 osm_anthro_df_2021_2022summary(lynx_land_mods$`500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(lynx, absent_lynx) ~ scale(lc_forest) + scale(lc_grassland) +
## scale(lc_developed) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 914.8 932.0 -452.4 904.8 227
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.1629 0.4037
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.85919 0.17480 -10.636 <2e-16 ***
## scale(lc_forest) -0.01256 0.06974 -0.180 0.857
## scale(lc_grassland) 0.09524 0.05849 1.628 0.103
## scale(lc_developed) -0.10278 0.08084 -1.271 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Moose
moose_land_mods <- osm_landscape_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(moose, absent_moose) ~
# Landscape classes that aren't correlated
scale(lc_forest) +
scale(lc_grassland) +
scale(lc_developed) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
moose_land_model.sel <- model.sel(moose_land_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
moose_land_model.sel ## Model cond((Int)) disp((Int)) cond(scale(lc_developed))
## 1 250 meter buffer -1.824370 + -0.38907200
## 2 500 meter buffer -1.811440 + -0.26517617
## 3 1250 meter buffer -1.811622 + -0.24854908
## 4 1000 meter buffer -1.809467 + -0.24500592
## 5 750 meter buffer -1.807044 + -0.22419410
## 6 1500 meter buffer -1.807871 + -0.20492713
## 7 1750 meter buffer -1.807510 + -0.20460227
## 8 2000 meter buffer -1.803424 + -0.17250345
## 9 2250 meter buffer -1.802767 + -0.17451218
## 10 5000 meter buffer -1.794867 + -0.03135968
## 11 2500 meter buffer -1.801511 + -0.16473681
## 12 2750 meter buffer -1.800720 + -0.15117920
## 13 4750 meter buffer -1.794206 + -0.04654219
## 14 3000 meter buffer -1.798543 + -0.13010136
## 15 4500 meter buffer -1.794334 + -0.06239861
## 16 4250 meter buffer -1.794461 + -0.06812523
## 17 4000 meter buffer -1.794621 + -0.07759938
## 18 3250 meter buffer -1.797146 + -0.11404443
## 19 3500 meter buffer -1.796185 + -0.10191078
## 20 3750 meter buffer -1.795196 + -0.08647021
## cond(scale(lc_forest)) cond(scale(lc_grassland)) df logLik AICc
## 1 -0.1894191876 -0.084560017 5 -435.5390 881.3435
## 2 -0.1252092690 0.012836269 5 -441.1781 892.6216
## 3 -0.1302412537 0.045986762 5 -441.2800 892.8256
## 4 -0.0972965295 0.019964245 5 -441.8496 893.9646
## 5 -0.0569759026 0.032593961 5 -442.5540 895.3735
## 6 -0.1308807651 0.041118183 5 -442.5726 895.4107
## 7 -0.1225948081 0.048505398 5 -442.6991 895.6637
## 8 -0.0978660613 0.023709654 5 -443.9016 898.0687
## 9 -0.0896738480 0.012999835 5 -443.9410 898.1476
## 10 0.0229644406 -0.150138388 5 -444.2650 898.7955
## 11 -0.0705255139 0.020287163 5 -444.2935 898.8524
## 12 -0.0541213807 0.037062218 5 -444.6862 899.6378
## 13 0.0170187161 -0.108580891 5 -444.9070 900.0794
## 14 -0.0322270115 0.022790635 5 -445.1435 900.5525
## 15 0.0152642073 -0.078029954 5 -445.1668 900.5992
## 16 0.0144043611 -0.067810754 5 -445.2068 900.6790
## 17 0.0091484173 -0.049862667 5 -445.3283 900.9221
## 18 -0.0159558138 0.012893112 5 -445.3831 901.0317
## 19 -0.0070072556 -0.002757181 5 -445.4782 901.2219
## 20 -0.0006681693 -0.022075498 5 -445.5523 901.3701
## delta weight Dataset
## 1 0.00000 9.876445e-01 osm_anthro_df_2021_2022
## 2 11.27815 3.512226e-03 osm_anthro_df_2021_2022
## 3 11.48212 3.171684e-03 osm_anthro_df_2021_2022
## 4 12.62118 1.794512e-03 osm_anthro_df_2021_2022
## 5 14.03005 8.871843e-04 osm_anthro_df_2021_2022
## 6 14.06727 8.708273e-04 osm_anthro_df_2021_2022
## 7 14.32023 7.673662e-04 osm_anthro_df_2021_2022
## 8 16.72522 2.305506e-04 osm_anthro_df_2021_2022
## 9 16.80412 2.216323e-04 osm_anthro_df_2021_2022
## 10 17.45207 1.602990e-04 osm_anthro_df_2021_2022
## 11 17.50895 1.558050e-04 osm_anthro_df_2021_2022
## 12 18.29438 1.052028e-04 osm_anthro_df_2021_2022
## 13 18.73592 8.436207e-05 osm_anthro_df_2021_2022
## 14 19.20908 6.658884e-05 osm_anthro_df_2021_2022
## 15 19.25569 6.505515e-05 osm_anthro_df_2021_2022
## 16 19.33556 6.250832e-05 osm_anthro_df_2021_2022
## 17 19.57864 5.535454e-05 osm_anthro_df_2021_2022
## 18 19.68821 5.240351e-05 osm_anthro_df_2021_2022
## 19 19.87843 4.764907e-05 osm_anthro_df_2021_2022
## 20 20.02667 4.424501e-05 osm_anthro_df_2021_2022summary(moose_land_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(moose, absent_moose) ~ scale(lc_forest) + scale(lc_grassland) +
## scale(lc_developed) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 881.1 898.3 -435.5 871.1 227
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.2284 0.4779
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.82437 0.20458 -8.917 < 2e-16 ***
## scale(lc_forest) -0.18942 0.07609 -2.489 0.0128 *
## scale(lc_grassland) -0.08456 0.06399 -1.321 0.1864
## scale(lc_developed) -0.38907 0.08615 -4.516 6.3e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Red fox
fox_land_mods <- osm_landscape_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(red_fox, absent_red_fox) ~
# Landscape classes that aren't correlated
scale(lc_forest) +
scale(lc_grassland) +
scale(lc_developed) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
fox_land_model.sel <- model.sel(fox_land_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
fox_land_model.sel ## Model cond((Int)) disp((Int)) cond(scale(lc_developed))
## 1 4750 meter buffer -4.311701 + 0.2865729
## 2 5000 meter buffer -4.314446 + 0.2946127
## 3 4500 meter buffer -4.305607 + 0.2758245
## 4 4250 meter buffer -4.288722 + 0.2665661
## 5 3750 meter buffer -4.268422 + 0.2491588
## 6 4000 meter buffer -4.264669 + 0.2688970
## 7 3500 meter buffer -4.258079 + 0.2469422
## 8 3250 meter buffer -4.258905 + 0.2281469
## 9 1500 meter buffer -4.286422 + 0.1720749
## 10 2750 meter buffer -4.250647 + 0.2308787
## 11 2500 meter buffer -4.280069 + 0.2042168
## 12 2250 meter buffer -4.292690 + 0.2029512
## 13 3000 meter buffer -4.238552 + 0.2446116
## 14 1750 meter buffer -4.291311 + 0.1625072
## 15 1250 meter buffer -4.242640 + 0.2204567
## 16 2000 meter buffer -4.282952 + 0.2067221
## 17 1000 meter buffer -4.226176 + 0.2698492
## 18 750 meter buffer -4.197991 + 0.3321050
## 19 500 meter buffer -4.125760 + 0.6055379
## 20 250 meter buffer -4.125957 + 0.4755105
## cond(scale(lc_forest)) cond(scale(lc_grassland)) df logLik AICc
## 1 -0.47066300 0.4440924 5 -172.6120 355.4895
## 2 -0.49014388 0.4374597 5 -172.7404 355.7463
## 3 -0.46005814 0.4369906 5 -173.1568 356.5791
## 4 -0.43624853 0.4273371 5 -174.1172 358.4999
## 5 -0.31343664 0.4867189 5 -174.8190 359.9035
## 6 -0.37452035 0.4290864 5 -175.0954 360.4563
## 7 -0.28257620 0.4828959 5 -175.6674 361.6002
## 8 -0.24611112 0.5047879 5 -176.0282 362.3219
## 9 -0.14489943 0.5400933 5 -176.1631 362.5917
## 10 -0.25524006 0.4595279 5 -176.6450 363.5555
## 11 -0.21586397 0.5171330 5 -176.7956 363.8568
## 12 -0.17676341 0.5379829 5 -176.8160 363.8975
## 13 -0.24897156 0.4498939 5 -176.8312 363.9278
## 14 -0.18913438 0.5304592 5 -177.2477 364.7609
## 15 -0.14633021 0.4692529 5 -177.3753 365.0160
## 16 -0.17391068 0.5171511 5 -177.4997 365.2649
## 17 -0.11051461 0.4438864 5 -178.2805 366.8265
## 18 0.04517619 0.4519380 5 -178.4801 367.2256
## 19 0.25203544 0.3412639 5 -180.4476 371.1607
## 20 0.02398044 0.2553588 5 -181.3288 372.9231
## delta weight Dataset
## 1 0.0000000 3.248046e-01 osm_anthro_df_2021_2022
## 2 0.2567835 2.856685e-01 osm_anthro_df_2021_2022
## 3 1.0896523 1.883680e-01 osm_anthro_df_2021_2022
## 4 3.0103732 7.209878e-02 osm_anthro_df_2021_2022
## 5 4.4140392 3.573763e-02 osm_anthro_df_2021_2022
## 6 4.9668439 2.710727e-02 osm_anthro_df_2021_2022
## 7 6.1107532 1.529991e-02 osm_anthro_df_2021_2022
## 8 6.8323776 1.066572e-02 osm_anthro_df_2021_2022
## 9 7.1022529 9.319391e-03 osm_anthro_df_2021_2022
## 10 8.0660043 5.755878e-03 osm_anthro_df_2021_2022
## 11 8.3672793 4.950973e-03 osm_anthro_df_2021_2022
## 12 8.4079915 4.851209e-03 osm_anthro_df_2021_2022
## 13 8.4383115 4.778220e-03 osm_anthro_df_2021_2022
## 14 9.2713792 3.150415e-03 osm_anthro_df_2021_2022
## 15 9.5265670 2.773029e-03 osm_anthro_df_2021_2022
## 16 9.7753938 2.448625e-03 osm_anthro_df_2021_2022
## 17 11.3370387 1.121542e-03 osm_anthro_df_2021_2022
## 18 11.7361119 9.186664e-04 osm_anthro_df_2021_2022
## 19 15.6712359 1.284271e-04 osm_anthro_df_2021_2022
## 20 17.4335926 5.320662e-05 osm_anthro_df_2021_2022summary(fox_land_mods$`4750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(red_fox, absent_red_fox) ~ scale(lc_forest) + scale(lc_grassland) +
## scale(lc_developed) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 355.2 372.5 -172.6 345.2 227
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 1.299 1.14
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.3117 0.5237 -8.233 <2e-16 ***
## scale(lc_forest) -0.4707 0.1928 -2.441 0.0147 *
## scale(lc_grassland) 0.4441 0.1794 2.476 0.0133 *
## scale(lc_developed) 0.2866 0.1200 2.388 0.0170 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1White-tailed deer
deer_land_mods <- osm_landscape_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~
# Landscape classes that aren't correlated
scale(lc_forest) +
scale(lc_grassland) +
scale(lc_developed) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# run model selection and save the results as a tibble for graphing use later
deer_land_model.sel <- model.sel(deer_land_mods) %>%
as.data.frame() %>%
rownames_to_column(var = 'Model') %>%
mutate(Dataset = deparse(substitute(osm_anthro_df_2021_2022)))
# look at model selection results
deer_land_model.sel ## Model cond((Int)) disp((Int)) cond(scale(lc_developed))
## 1 4500 meter buffer -0.1623813 + 0.20187598
## 2 4250 meter buffer -0.1625427 + 0.19591693
## 3 4750 meter buffer -0.1620244 + 0.22303896
## 4 4000 meter buffer -0.1635226 + 0.19381588
## 5 5000 meter buffer -0.1623011 + 0.23067503
## 6 3750 meter buffer -0.1641042 + 0.19652830
## 7 3500 meter buffer -0.1641705 + 0.19465875
## 8 3250 meter buffer -0.1641689 + 0.19223412
## 9 3000 meter buffer -0.1631662 + 0.19472744
## 10 2750 meter buffer -0.1625679 + 0.19934091
## 11 2500 meter buffer -0.1625756 + 0.19855991
## 12 2250 meter buffer -0.1623364 + 0.20390809
## 13 2000 meter buffer -0.1617604 + 0.19848624
## 14 1750 meter buffer -0.1616216 + 0.18838780
## 15 1500 meter buffer -0.1628144 + 0.16268304
## 16 1250 meter buffer -0.1615885 + 0.16221466
## 17 1000 meter buffer -0.1598991 + 0.16354671
## 18 750 meter buffer -0.1614725 + 0.10971171
## 19 250 meter buffer -0.1670141 + -0.04939048
## 20 500 meter buffer -0.1652200 + 0.01759524
## cond(scale(lc_forest)) cond(scale(lc_grassland)) df logLik AICc
## 1 -0.20538963 0.37068831 5 -587.9308 1186.127
## 2 -0.19218942 0.36213244 5 -589.2947 1188.855
## 3 -0.21075061 0.34441889 5 -589.3299 1188.925
## 4 -0.17740529 0.35435141 5 -590.5906 1191.447
## 5 -0.22074357 0.31717359 5 -591.1398 1192.545
## 6 -0.16596432 0.34093999 5 -592.0095 1194.284
## 7 -0.15778893 0.32926474 5 -593.4363 1197.138
## 8 -0.15234248 0.32505954 5 -593.8628 1197.991
## 9 -0.14746745 0.30920697 5 -595.4702 1201.206
## 10 -0.14242312 0.28807224 5 -597.4595 1205.184
## 11 -0.13978800 0.27618068 5 -599.0260 1208.318
## 12 -0.13605774 0.25025524 5 -601.1461 1212.558
## 13 -0.12928230 0.23720599 5 -602.3431 1214.952
## 14 -0.12274787 0.22306219 5 -603.8423 1217.950
## 15 -0.12319161 0.21265628 5 -606.0623 1222.390
## 16 -0.10650333 0.19348297 5 -608.7951 1227.856
## 17 -0.07477042 0.14695476 5 -614.2262 1238.718
## 18 -0.08782437 0.12648591 5 -617.4000 1245.065
## 19 -0.18923240 0.03773632 5 -618.6593 1247.584
## 20 -0.13121110 0.08187522 5 -620.2648 1250.795
## delta weight Dataset
## 1 0.000000 6.108356e-01 osm_anthro_df_2021_2022
## 2 2.727884 1.561607e-01 osm_anthro_df_2021_2022
## 3 2.798154 1.507693e-01 osm_anthro_df_2021_2022
## 4 5.319680 4.273371e-02 osm_anthro_df_2021_2022
## 5 6.417984 2.467612e-02 osm_anthro_df_2021_2022
## 6 8.157393 1.034116e-02 osm_anthro_df_2021_2022
## 7 11.010977 2.482682e-03 osm_anthro_df_2021_2022
## 8 11.863979 1.620668e-03 osm_anthro_df_2021_2022
## 9 15.078879 3.247785e-04 osm_anthro_df_2021_2022
## 10 19.057402 4.442855e-05 osm_anthro_df_2021_2022
## 11 22.190518 9.275017e-06 osm_anthro_df_2021_2022
## 12 26.430608 1.113245e-06 osm_anthro_df_2021_2022
## 13 28.824693 3.362961e-07 osm_anthro_df_2021_2022
## 14 31.823070 7.509873e-08 osm_anthro_df_2021_2022
## 15 36.262932 8.156966e-09 osm_anthro_df_2021_2022
## 16 41.728719 5.304545e-10 osm_anthro_df_2021_2022
## 17 52.590774 2.322645e-12 osm_anthro_df_2021_2022
## 18 58.938417 9.718735e-14 osm_anthro_df_2021_2022
## 19 61.457022 2.758682e-14 osm_anthro_df_2021_2022
## 20 64.667930 5.539388e-15 osm_anthro_df_2021_2022Save model results
We also want to save the output from the model.sel funciton for each species as one csv file for use plotting later like we did with the previous analyses
# provide list of model.sel data frames from global analysis
land_model.sel_data <- list(
bear = bear_land_model.sel,
caribou = caribou_land_model.sel,
coyote = coyote_land_model.sel,
fisher = fisher_land_model.sel,
wolf = wolf_land_model.sel,
lynx = lynx_land_model.sel,
moose = moose_land_model.sel,
fox = fox_land_model.sel,
deer = deer_land_model.sel
) %>%
# use purrr to combine data and extract species names to use as a column
map_dfr(~.x %>%
mutate(species = deparse(substitute(.x))),
.id = "species")And save this for use later
write_csv(land_model.sel_data,
'data/processed/OSM_glm_land_model_sel_data.csv')In summary it does matter whether you are looking at landscape or anthropogenic features which spatial scale you use. Only two species (lynx and moose) had spatial scales for both top models within 1000m (1km). There were also more well-defined top models (delta AIC greater than 2.00) when you subset data to specifically look at anthro or landscape features.